Microwave Remote Sensing has developed rapidly during the past period, becoming a leading method in remote sensing techniques, since its unique features, capability to penetrate clouds and some extent rain due to it independence from the sun as a source of illumination and other important properties different from visible and IR frequency bands, Microwave Remote Sensing becomes a key means for obtaining information from ocean, atmosphere, and land in global scale. The frequency range using for microwave remote sensing is extending to millimeter and submillimeter bands. To meet the wide requirements, China enhanced the microwave remote sensing research during the past 30 years and has got many achievements. In this paper, the microwave remote sensing activities and its achievement, the stage of the arts, the space program for microwave remote sensing are described, it also gives some strategic suggestions in future development, discusses some concepts of space borne system and gives the research areas suggested to be given priority to develop in national R&D plan. A new concept of remote sensing -- A comprehensive electromagnetic sensing system and a virtual space remote sensing are being suggested to improve remote sensing capability.

A massive sandstorm has enveloped most northern China during the spring season 2002. Monitoring the evolution of sandstorm and desertification has become one of most serious problems for China's environment. Since 1989, one of the most advanced and operational passive microwave sensors is the DMSP SSM/I (special sensor microwave imager) operated at seven channels (19, 37, 85GHz with vertical and horizontal polarization and 22GHz with vertical polarization only). In the paper, the sandstorm and desertification indexes, SDI and DI, are derived from the radiative transfer equation, and are employed with multi-channel measurements of the DMSP SSM/I for monitoring the sandstorm and desertification in Northern China. Some SSM/I data in 1997 and 2001 are employed. The algorithm of the Getis statistics is developed to categorize the spatial correlation and its evolution during these days. It is demonstrated that the SSM/I indexes, SDI and DI, and its Getis statistics are well applicable for monitoring the sandstorm and desertification.

Recent attempts to determine the response of the tropical hydrological cycle to climate forcing have been hampered by large discrepancies in the variability of available long-term satellite rainfall datasets over seasonal to interannual time scales. From investigations of regional and time-dependent changes in the structure of precipitation systems it is apparent that variations in precipitation systems due to differences in the meteorological regimes produce biases in satellite retrievals. This can have severe consequences for studies of climate variability since changes in these "climate regime" biases over seasonal or interannual time scales can produce large errors in the observed variability. Using data from the Tropical Rainfall Measuring Mission (TRMM) we have attempted to identify and understand how changes in the structure of precipitation systems result in biases in various single sensor satellite retrievals. Evidence of differences in the relative content of ice versus liquid water, systematic changes in the height of the liquid water column, and changes in the relationship of low-level liquid water content to surface rainfall have been found, which can impact passive microwave rainfall retrievals. In addition, evidence of significant changes in the mean drop diameter between these regions may lead to significant biases in retrievals from the precipitation radar. Although TRMM has produced improvements in estimates of zonal mean rainfall, we must address this issue of time-dependent systematic biases associated with climate regimes if we wish to develop more accurate retrievals for either regional applications or global mean estimates suitable for studies of climate variability.

Although space-borne passive microwave techniques have been used in global precipitation remote sensing with preliminary success, improvement of retrieval accuracy is still an active subject. As part of Chinese National High-Tech Research and Development Program for Space Technology, an improving method for remote sensing of rainfall distribution over ocean area is investigated. In this paper, the vertical structures of cloud model are established, and the radiative transfer models are briefly introduced, also some comparison results between simulation and observation are discussed.

The Advanced Microwave Scanning Radiometers (AMSR) are dual-polarized microwave radiometers having channel frequencies ranging from 6.9 GHz to 89 GHz, and were designed to retrieve global information on precipitation, sea surface temperature, oceanic surface winds and integrated cloud water and water vapor, vegetation, sea ice, and snow cover. Two AMSR's have been built by Mitsubishi Electric Corporation for the National Space Development Agency of Japan. The first instrument (AMSR-E) was launched in May 2002 on NASA's Aqua satellite. The second will be launched on the Japanese ADEOS-II satellite. The AMSRs provide the highest spatial resolution yet attained for a civilian spaceborne microwave sensor, with spatial resolutions ranging from 5 km at 89 GHz to 60 km at 6.9 GHz. A distributed array of six (seven for ADEOS-II AMSR) feedhorns are illuminated by a 1.6 m diameter offset parabolic reflector on AMSR-E, and a 2.0 m diameter reflector on AMSR for ADEOS-II. While National Space Development Agency of Japan (NASDA) is responsible for the calibration of both AMSRs' data, for AMSR-E, science software for the retrieval of the various geophysical parameters has been independently developed by NASDA- and NASA-funded researchers. This software has been implemented for routine near-real time processing in both Japan and the United States. A future goal -- within two years -- is the development of joint algorithms for processing data from both AMSRs. Extensive product validation efforts, involving many different countries, are discussed. Initial data from AMSR-E are also presented.

Concept and expected performance of cloud profiling radar (CPR) for EarthCARE are described based on preliminary design study conducted to date. High sensitivity and Doppler capability are two significant new features in this CPR. Particularly, Doppler capability is the first attempt to spaceborne atmospheric radar, which requires great efforts in technical development and feasibility validation. We have developed a new numerical simulation method to assess Doppler velocity accuracy applicable to this application, and results are compared with conventional approximation method. Validity and limitation of the approximation method are indicated from comparison with numerical method. It is shown that requirements to radar sensitivity and Doppler measurements will be satisfied. However, because these requirements to CPR are very tough, further detailed study on both design optimization and assessment technique development are necessary. Under radar operation with very high pulse repetition frequency (PRF) required in this CPR, surface clutter interference caused through antenna sidelobes is an important issue. Analysis on this issue and preliminary requirements to the antenna sidelobes are also discussed.

The Earth Exploration Satellite Service requires a scientifically determined set of microwave frequency bands in which passive remote sensing of the Earth's surface and atmosphere can be performed. Those bands are defined by the physical laws of the atmosphere and are one of the Earth's important natural resources. However, due to the inflation of the civil and military needs (mobile phones, TV, Internet, radar on cars etc.), many areas of the microwave spectrum could become useless for passive microwave measurements. The reason for this is man-made interference which can render a passive sensing frequency band utterly useless either from in-band or out-of-band emissions. The World Meteorological Organisation (WMO), together with most of the space agencies around the world, has defined an extensive list of frequency bands that need to be forever protected known as the "Do or Die" list. This list does not duplicate the frequency needs for any given use. It's the minimum set of bands to monitor the Earth's environment, including weather forecasts, hydrology and global change among other vital applications.

An airborne multi-function microwave remote sensing system has been developed in order to verify the design and performance of future Chinese spaceborne system. Future Chinese spaceborne microwave remote sensing system is for ocean research, atmosphere research and soil moisture content monitoring. Like spaceborne system, the airborne system also includes altimetry, scatterometry and radiometry functions. There are five operate modes: altimetry mode, scatterometry mode, radiometry mode, altimetry and radiometry mode, scatterometry and radiometry mode. The operate mode can be changed by program. There are five channels in radiometry mode. The altimetry mode, the scatterometry mode and the second channel of radiometry mode operate at the same Ku band. In the airborne system, the scatterometry mode uses two pyramidal horn antennae. One is for horizontal polarization. The other is for vertical polarization. The horizontal polarization antenna is also used in the altimetry mode. The radiometry mode uses five pyramidal horn antennae. Flight experiments have been conducted on southern sea of China. The results verify the design and performance of the airborne multi-function microwave remote sensing system. They also show that the design of future Chinese spaceborne system is practicable. In this paper, the principle of airborne system is briefly introduced. Flight experiments and results are described.

Global rainfall is the primary distributor of latent heat through atmospheric circulation. This important atmospheric parameter can only be measured reliably from space. The on-going Tropical Rainfall Measuring Mission (TRMM) is the first space based mission dedicated to advance our understanding of tropical precipitation patterns and their implications on global climate and its change. The Precipitation Radar (PR) aboard the satellite is the first radar ever flown in space and has provided exciting, new data on the 3-D rain structures for a variety of scientific applications. The continuous success of TRMM has led to new development of the next generation of spaceborne satellites and sensors for global rainfall and hydrological parameter measurements. From science and cost efficiency prospective, these new sensing instruments are expected to provide enhanced capabilities and reduced consumption on the spacecraft resources. At NASA, the Earth Science Enterprise has strengthened its investment on instrument technologies to help achieving these two main goals and to obtain the best science values from the new earth science instruments. It is with this spirit that a notional instrument concept, using a dual-frequency rain radar with a deployable 5-meter electronically-scanned membrane antenna and real-time digital signal processing, is developed. This new system, the Second Generation Precipitation Radar (PR-2), has the potential of offering greatly enhanced performance accuracy while using only a fraction of the mass of the current TRMM PR. During the last two years, several of the technology items associated with this notional instrument have also been prototyped. In this paper, the science rationales, the instrument design concept, and the technology status for the PR-2 notional system will be presented.

The design of the China Imaging ALTimeter (CIALT) and the flight experiment of its airborne model are presented in this paper. The system is aimed for providing observation measure for both oceanic applications and continental topographic mapping in the future. The motivation of this project is to develop a three dimensional imager fitted for small satellites with small volume, mass and power consumption. An experimental airborne model of the CIALT has been developed for verifying the design concept. The CIALT integrates three techniques together, i.e. the height measurement and tracking technique of traditional radar altimeter used for ocean applications, the synthetic aperture technique and the interferometric technique. A robust height tracker has been designed for meeting the requirements of both oceanic surfaces and continental surfaces (including surfaces of ice continent). The synthetic aperture technique is used for achieving a higher azimuthal resolution along the cross range direction compared with that of a traditional altimeter. The interferometric technique is used for retrieving the height information corresponding to each image pixel and for boresight angle correction of the antennas, which is crucial for accurate height measurement. The CIALT is different from other proposed imaging altimeters, such as SAR altimeter and scanning altimeter, in which no height tracker is involved. Some key technologies regarding the development of imaging altimeter are addressed, such as the antenna design, the transmitter, the receiver and the robust tracking algorithm.

Thermodynamic profiling provides continuous temperature, humidity and cloud liquid profiles during clear and cloudy conditions. The thermodynamic profiler radiometrically observes microwave radiation intensity at multiple frequencies, along with infrared and surface meteorological measurements. Historical radiosonde and neural network or regression methods are used for profile retrieval. Wind profiling radar provides horizontal winds. We compare radiosonde, thermodynamic and wind soundings to evaluate continuous profiling accuracy. Forecast and observed thermodynamic profiles are also compared. Thermodynamic profiling, particularly when combined with wind profiling radar and advanced assimilation methods, provides continuous soundings needed for improved local high resolution modeling and forecasting. We also describe "slant" observations of integrated GPS signal delay and their potential to extend local forecast improvements to regional scale. Applications include improved forecasting of high resolution dispersion and transport, short-term precipitation and fog.

During the past decade Doppler radar profilers that operate near 1 GHz and 3 GHz have been developed at the NOAA Aeronomy Laboratory for use in dynamics and precipitation research. The profilers have been used extensively in numerous field campaigns during the past decade. In the presence of precipitating clouds, backscattering from hydrometeors is dominant and the Doppler velocity provides a measure of the fall velocity of hydrometeors. Profiler observations yield time height cross-sections of equivalent reflectivity, Doppler velocity and spectral width that illustrate the evolution of precipitating clouds systems. The vertical structure of these parameters has been used to classify the precipitating cloud systems into several different categories. These observations document the prevalence of deep anvil cloud systems over the Pacific warm pool region. They also show the relative abundance of rainfall from stratiform and convective components of precipitating cloud systems and the continuous observations reveal the diurnal evolution of the precipitating clouds over the profiler. The profiler observations provide important information for the calibration and validation of precipitation measurements by other instruments and platforms. For example, direct comparisons of profiler reflectivities with scanning radar reflectivities provide a direct means for calibration of scanning radars. The profilers are calibrated with a collocated disdrometer. An important objective of the profiler observations is to retrieve drop-size distributions and to determine the variability of the drop-size distributions in diverse precipitating cloud systems. Recent developments provide optimism that drop-size distribution retrievals can be made by profilers operating at 1 GHz or 3 GHz without complementary measurement of vertical air motions.

The Constellation Observing System for Meteorology, Ionosphere and Climate (COSMIC) is a joint Taiwan-U.S. space mission, with a plan to launch a constellation of six micro-satellites in late 2005. Each satellite will carry three instruments: a Global Positioning System (GPS) Radio Occultation (RO) receiver, a Tiny Ionospheric Photometer (TIP), and a Tri-Band Beacon (TBB). The COSMIC constellation will provide up to 3,000 RO soundings that are distributed relatively uniformly around the Earth. The raw measurements made by the GPS RO receivers are the phase and amplitude of the GPS radio signals (L-band with wavelengths L₁ ~19.0 cm and L₂ ~ 24.4 cm), which can be used to derive the vertical profiles of temperature, moisture and electron density. The TIP and TBB instruments will provide additional ionospheric measurements. The COSMIC data from these three instruments are expected to make a significant impact on global weather prediction, climate and ionosphere monitoring and research. This paper presents (1) an overview of the COSMIC system; (2) CDAAC results from two recent GPS RO missions, CHAMP and SAC-C; and (3) the potential impact of COSMIC data on numerical weather prediction as indicated by recent observing system simulation experiments (OSSEs).

NASA's Earth Science Enterprise has identified the need for improved measurement of snow properties and frozen soils via a space-flight mission within the next decade. Microwave sensors appear ideal to measure these properties. Measurements of the Earth's surface in the microwave spectral regions can be largely insensitive to weather conditions and solar illumination, which is especially important during cold seasons. Both active and passive microwave sensors have demonstrated sensitivity to snow properties and the freeze/thaw status of soils. Microwave signal response is influenced by snow depth, density, wetness, crystal size and shape, ice crusts and layer structure, surface roughness, vegetation characteristics, soil moisture, and soil freeze/thaw status. These characteristics make microwave remote sensing attractive for providing spatially distributed information to improve and update land surface models for cold regions, either through assimilation of state-variable information estimated from microwave remote sensing observations using inversion algorithms, or through direct assimilation of microwave remote sensing data themselves. At the same time, the sensitivity of microwave signal response to several snow, soil, and vegetation characteristics also complicates the interpretation and analysis of these data. To better understand microwave remote sensing for measurement of snow and frozen soil properties, NASA is conducting the Cold Land Processes Field Experiment (CLPX). The CLPX is a large field experiment being conducted primarily over a two-year period (2002 and 2003) in Colorado, U.S.A. The purpose of the CLPX is to develop the quantitative understanding, models, and measurements necessary to extend our local-scale understanding of water fluxes, storage, and transformations to regional and global scales. Of particular importance is the development of a strong synergism between process-oriented understanding, land surface models and microwave remote sensing. Objectives of the CLPX include evaluation and improvement of algorithms for retrieving snow and frozen soil information from active and passive microwave sensors, evaluating the effects of sensor spatial resolution on retrieval skill, coupling forward microwave radiative transfer schemes to distributed snow/soil models to improve assimilation of microwave remote sensing data, and to develop sensor specifications for a new space-flight mission to measure cold land processes. This paper discusses the data sets collected during the CLPX-2002 to support these objectives.

Snow cover is an important variable for climate and hydrologic models due to its effects on energy and moisture budgets. Seasonal snow can cover more than 50% of the Northern Hemisphere land surface during the winter resulting in snow cover being the land surface characteristic responsible for the largest annual and interannual differences in albedo. Passive microwave satellite remote sensing can augment measurements based on visible satellite data alone because of the ability to acquire data through most clouds or during darkness as well as to provide a measure of snow depth or water equivalent. It is now possible to monitor the global fluctuation of snow cover over a 24 year period using passive microwave data (Scanning Multichannel Microwave Radiometer (SMMR) 1978-1987 and Special Sensor Microwave/Imager (SSM/I), 1987-present). Evaluation of snow extent derived from passive microwave algorithms is presented through comparison with the NOAA Northern Hemisphere snow extent data. For the period 1978 to 2002, both passive microwave and visible data sets show a smiliar pattern of inter-annual variability, although the maximum snow extents derived from the microwave data are consistently less than those provided by the visible statellite data and the visible data typically show higher monthly variability. During shallow snow conditions of the early winter season microwave data consistently indicate less snow-covered area than the visible data. This underestimate of snow extent results from the fact that shallow snow cover (less than about 5.0 cm) does not provide a scattering signal of sufficient strength to be detected by the algorithms. As the snow cover continues to build during the months of January through March, as well as on into the melt season, agreement between the two data types continually improves. This occurs because as the snow becomes deeper and the layered structure more complex, the negative spectral gradient driving the passive microwave algorithm is enhanced. Trends in annual averages are similar, decreasing at rates of approximately 2% per decade. The only region where the passive microwave data consistently indicate snow and the visible data do not is over the Tibetan Plateau and surrounding mountain areas. In the effort to determine the accuracy of the microwave algorithm over this region we are acquiring surface snow observations through a collaborative study with CAREERI/Lanzhou. In order to provide an optimal snow cover product in the future, we are developing a procedure that blends snow extent maps derived from MODIS data with snow water equivalent maps derived from both SSM/I and AMSR.

This paper describes an approach to estimate global snow cover using satellite passive microwave data. Snow cover is detected using the high frequency scattering signal from natural microwave radiation, which is observed by passive microwave instruments. Developed for the retrieval of global snow depth and snow water equivalent using Advanced Microwave Scanning Radiometer EOS (AMSR-E), the algorithm uses passive microwave radiation along with a microwave emission model and a snow grain growth model to estimate snow depth. The microwave emission model is based on the Dense Media Radiative Transfer (DMRT) model that uses the quasi-crystalline approach and sticky particle theory to predict the brightness temperature from a single layered snowpack. The grain growth model is a generic single layer model based on an empirical approach to predict snow grain size evolution with time. Gridding to the 25 km EASE-grid projection, a daily record of Special Sensor Microwave Imager (SSM/I) snow depth estimates was generated for December 2000 to March 2001. The estimates are tested using ground measurements from two continental-scale river catchments (Nelson River and the Ob River in Russia). This regional-scale testing of the algorithm shows that for passive microwave estimates, the average daily snow depth retrieval standard error between estimated and measured snow depths ranges from 0 cm to 40 cm of point observations. Bias characteristics are different for each basin. A fraction of the error is related to uncertainties about the grain growth initialization states and uncertainties about grain size changes through the winter season that directly affect the parameterization of the snow depth estimation in the DMRT model. Also, the algorithm does not include a correction for forest cover and this effect is clearly observed in the retrieval. Finally, error is also related to scale differences between in situ ground measurements and area-integrated satellite estimates. With AMSR-E data, improvements to snow depth and water equivalent estimates are expected since AMSR-E will have twice the spatial resolution of the SSM/I and will be able to characterize better the subnivean snow environment from an expanded range of microwave frequencies.

Seasonal snow cover is an important component in investigations of land-surface climate and hydrology. This study summarizes the recent progresses of using multi-parameter SAR in estimating snow properties. These progresses include (1) mapping snow cover, (2) estimating snow wetness, (3) estimating snow density, and (4) estimating snow depth and grain size. We demonstrate these progresses using SIR-C/X-SAR image data that was taken in 1994 in Mammoth Mtn., California, U.S.A.

So far, there is not an operational algorithm to estimate the snow water equivalent from passive microwave remote sensing data (SSM/I) in the Tibetan Plateau. In this study the SSM/I brightness temperature data in January 1993 are used to estimate SWE at this region. The frequencies of SSM/I data are used to retrieval snow depth are 19 and 37GHz in horizontal polarization. The results have shown all existing algorithms overestimated the snow depth in the Tibetan plateau. This paper analyzed the reasons of overestimation of snow depth from several aspects, such as the water content of snowpack, large water bodies (e.g. lakes), and the abnormal field snow depth data. After eliminating some futile data (including the passive microwave brightness temperature values and snow depth data in the weather stations), an improved algorithm has been established to retrieval the snow depth from the difference of 19 and 37GHz brightness temperature in horizontal polarization. Here, snow density is obtained by a time function of fresh snow density. The snow depth and density were converted to the snow water equivalent, and are regarded as the ground truth. In finally, the TB vertically polarized differences of 19 and 37GHz are regressed with the SWE. Using the statistical method, a simple and practical algorithm is developed to estimate the snow water equivalent from the differences of 19 and 37GHz in vertical polarization.

The National Space Development Agency of Japan Advanced Microwave Scanning Radiometer (AMSR-E) was successfully launched on NASA's EOS Aqua spacecraft on May 4, 2002. This new state-of-the-art satellite radiometer will provide a wider range of frequencies and twice the spatial resolution than is currently available with the DMSP SSM/I. New sea ice algorithms have been developed for use with the AMSR-E. The standard sea ice products to be provided include sea ice concentration at spatial resolutions of 12.5 km and 25.0 km, snow depth on sea ice at a spatial resolution of 12.5 km, and sea ice temperature at a spatial resolution of 25 km. This paper provides a summary of our plans to validate the AMSR-E sea ice products in the Arctic. The overall validation program consists of three elements: satellite data comparisons, coordinated satellite/aircraft/surface comparisons, and a modeling and sensitivity analysis component. The first coordinated satellite/aircraft/surface Arctic campaign is planned for March 2003. A second campaign is planned for March 2005.

Dual-frequency multi-polarization Spaceborne Synthesis Aperture Radar (SAR) can obtain multi-polarization images of large ground area synchronously. These images include multiple information of the ground target. It benefits target detection, identification, and classification. For high-orbit multi-polarization Spaceborne SAR, it’s difficult to solve the problem of range ambiguity when the look angle is variable. In this paper, ambiguity property of multi-polarization Spaceborne SAR system is analyzed. And Polarization Time Division (PTD), Polarization Frequency Division (PFD), and Polarization Code Division (PCD) characteristics of multi-polarization Spaceborne SAR system are compared in detail. Ground experiment system of Dual-frequency multi-polarization Spaceborne SAR and its performance are presented.

In this paper, the Monte Carlo Method is applied to study the vegetation scattering and its low-grazing characteristics. Based on the Two-layer canopy scattering model, phase difference of the discrete scatterers and volume-surface scattering interaction are taken into account, the scattering coefficient is simulated by the Monte Carlo method. The numerical results are compared with the experiments and the corresponding theory, which justify the backscattering enhancement and grazing incidence characteristic properly.

SIR-C images taken over the Sarobetsu test site are analyzed using the target decomposition method to characterize the scattering mechanisms and relate them with the surface conditions. The scattering mechanisms assumed are double bounce scattering, Bragg scattering, odd bounce scattering, and cross scattering. The SIR-C data analyzed was taken on 10 April 1994 at the incident angle of 23.8 degrees. Two target areas (forest and pastureland) are extracted from the images, where in the former double bounce and cross scatterings must exceed the others, while in the latter odd bounce and/or Bragg scattering is expected to dominate, by referring to aerial photographs. The analysis results agree well with the expectations with one exception, i.e. the most significant mechanism in the forest is the odd bounce scattering. Analysis is made on two additional areas extracted from the mountain and the wildland, and it is confirmed that the former result is similar to that of the forest and the latter result agrees with that of the pastureland. It suggests the potential of the target decomposition method to infer sruface conditions.

The Mueller matrix solution and eigen-analysis of the coherency matrix for completely polarimetric scattering have been applied to analysis of the SAR (synthetic aperture radar) imagery. Co-polarized and cross-polarized backscattering for any polarized incidence can be obtained. The polarization index is usually defined as a parameter to classify the difference between polarized scattering signatures from the terrain surfaces. In this paper, the eigen-values of the coherency matrix and information entropy are derived to directly relate with measurements of the co-polarized and cross-polarized indexes. Thus, it combines the Mueller matrix simulation, the information entropy of the coherence matrix, and two polarization indexes together and yields a quantitative evaluation for surface classification in the SAR imagery. This theory is applied to analysis of the AirSAR images and fields measurements.

The real time rain estimates from the TRMM/TMI and the SSM/I Goddard Profiling Alforithm (GPROF), along with the Climate Prediction Center real time IR rain estimates, have been merged to a preparatory version of the TRMM global 3-hourly rain product. This paper discusses the issue of merging NOAA/AMSU rain estimates as an addition into this preparatory product and provides comparisons of rainfall among TMI, SSM/I GPROF and AMSU products. The orbit data of the three microwave sensors were aggregated to 3-hourly gridded rain products with horizontal resolution of 0.25°. The histogram data based on the coincident rainfall among TMI, SSM/I and AMSU estimates from the 3-hourly data series showed about +50% bias of AMSU rain as compared to the SSM/I-TMI rain over tropical ocean. However, over land, the AMSU data showed about -20% bias as compared to the SSM/I-TMI products. The correlation between TMI and AMSU oceanic data is about 0.5. In order to justify the results from microwave rainfall estimates, the TMI rain estimate were compared with ground validation data for a 4-year period. The results indicate a lower (-15%) TMI rain amount over ocean and a higher (+25%) TMI estimate over land as compared to the ground observation.

Rainfall retrieved from space-borne instruments has been accepted as reliable and accurate by a majority of the atmospheric community. One of the Tropical Rainfall Measuring Mission (TRMM) facility rain algorithms is the passive microwave-based rain retrieval algorithm (2A-12). In order to introduce latent heating as a product of 2A-12, many improvements have been made to the current Version 5 algorithm. This paper shows how these modifications impact retrieved surface rainfall rate and latent heating estimates. Comparisons indicate that the error statistics for the prototype Version 6 2A-12 are similar to those of Version 5 at footprint-scale and 30-km resolution; however, consistent latent heating vertical profiles are now obtainable. Preliminary comparisons to dual-doppler radar-based estimates show similar heating structures, but further study will be required to establish the general credibility of 2A-12 latent heating estimates.

Recent improvements in a method for remotely sensing precipitation and latent heating distributions based upon satellite-borne, passive microwave radiometer observations are summarized. In applications to synthetic data, estimated rainfall rates at sensor footprint-scale (14 km) are subject to significant random errors, but these errors are substantially reduced by spatial averaging. After spatial-averaging, rain rate and latent heating profile estimates exhibit biases that arise from a lack of specificity in the information contained in the microwave radiance data. The retrieval method is applied to observations from the Tropical Rainfall Measuring Mission Microwave Radiometer (TMI). Retrieved instantaneous precipitation and heating distributions show general self-consistency and delineate plausible storm structures in an application to TMI observations of a mesoscale convective system over the tropical North Atlantic. Well-known climatological distributions of rainfall are reproduced by global, monthly-mean TMI precipitation estimates from July 2000. Zonal-mean heating profiles in the Tropics from the same period exhibit a primary maximum of heating near 7 km altitude and a secondary peak near 3 km, while at higher latitudes in the Southern Hemisphere, a vertical structure with heating aloft and cooling at lower altitudes is derived.

The developments of Typhoon Lingling or TY27W in the South China Sea were studied utilizing the satellite data such as GMS, QuikSCAT/SeaWinds, TRMM, SSM/I and weather maps. Originating in the Philippines Sea, TY 27W was tracked over Central Philippines into the South China Sea making landfall near Qui Nhon, Vietnam. The data showed the pattern of development of wind and atmospheric pressure from Tropical Storm since 6 November 2001 to Typhoon Lingling on 9 to 12 November 2001. This cyclone entered the South China Sea as a weak tropical storm, but rapidly intensified while tracking along the southern edge of a strong northeasterly monsoon surge. TY 27W was a weak system as it passed over the resort island of Camiguin in central Philippines, but was tracked very slowly and drenched the area with torrential rains. After landing on the Vietnamese coast on 12 November 2001, the strength of Typhoon Lingling has declined into Tropical Depression and disappeared from the weather map since 12 November 2001 at 12 hour 9 minute 25 second (GMT). CNN reported that 171 people were confirmed dead, 118 missing, and thousands of homes damaged in the areas affected. TY 27W also produced heavy rains and high winds over central Vietnam. CNN reported 18 deaths, hundreds of injuries, and over 1000 homes destroyed in Vietnam.

We develop two methods of retrieving sea surface wind speed. One is a physical method using 19.35GHz brightness temperatures with vertical and horizontal polarization. The other is a semi-statistic method based on sea surface emissivity models. We compare the retrieved results with in-situ buoy data and with the existing wind retrieval model, namely Wentz'92 and Goodberlet'90.

An observational method has been proposed to sample echo data with high range resolutions using a ground-based meteorological radar. Utilizing this method, a quantity of rain echo data with a high range resolution of 125 m was obtained by using an X-band meteorological radar. The computation of rain nonuniformity strength using this high resolution radar data shows that the nonuniformity is significant and even in an instantaneous field of view (IFOV) of 1 km, the reflectivity excursion above 10 dB is common. The simulation of the nonuniform beam filling (NUBF) error of the path-averaged rainrate derived from the path-integrated attenuation measured by the spaceborne radar has been also implemented using this data. The results show that the rainrate encounters mainly underestimation and cannot be neglected, even in 0.5 km IFOV, it can reach over 50%. The correlation analyses show that the rainrate error and the true rainrate have a power relationship with some correlation, which might be used to correct this error partially. The simulation also shows that it is very important to use the high resolution data in studying the NUBF error of the next generation spaceborne radar with a higher across beam resolution (e.g. below 3 km).

The altitude of the Tropical Rainfall Measuring Mission (TRMM) satellite was raised from 350km to 402.5km in August 2001 in order to extend its lifetime. The minimum detectable value of Z-factor after the boost is 1.2dB higher. We compared the actual PR products before and after the altitude increase using statistical methods in order to verify the algorithms and the Precipitation Radar (PR) rain products after the orbit was raised, and to confirm the influence of raising the orbit on PR rain products. The reflectivity factor histograms do not exhibit any significant changes after the raising of the satellite, except for a 1.2dB increase of the minimum detectable value. The results are consistent with the estimation before the raising. The monthly global average of the conditional rain rate in 3A25 product increased 0.2 mm/h after the orbit raising. This result corresponded to the simulated rainfall average estimated from the 1C21 product before the raising. Changes in monthly global rainfall average of unconditional rain, height of rainfall and height of bright band due to the orbit raising were not significant. This result shows that the orbit change had little influence on the PR estimation.

A primary goal of the South China Sea Monsoon Experiment (SCSMEX, 1998), a major field campaign of Tropical Rainfall Measuring Mission (TRMM), is to define the initiation, structure, evolution and dynamics of precipitation processes associated with the onset of the South China Sea (SCS) summer monsoon. Information from SCSMEX will be used for initialization and validation of cloud-resolving models and passive microwave retrieval algorithms. In this study, dual-Doppler radar analysis technique combining with the polarimetric radar data analysis are used to investigate the development and structure of a vigorous squall line system observed on 24 May 1998. Comparing to the tropical squall lines observed in other regions, this narrow squall line system had some interesting features including: 1) with maximum reflectivity over 55 dBZ, this squall line system has little stratiform rain, 2) the small area of stratiform rain was ahead instead of trailing to the convective line, and 3) rather than a narrow ribbon of vertical velocity maximum near the leading edge, this system has an elongate vertical velocity maximum in the rear part of the system. Polarimetric radar inferred microphysical and rainfall properties are placed in the context of the mesoscale morphology and dual-Doppler derived kinematics for this vigorous squall line.

A cloud profiling radar transmitting frequency-modulated continuous wave (FM-CW) at 95 GHz is developed for ground-based observations. Millimeter wave at 95 GHz is used to realize high sensitivity to small cloud particles. Two 1m-diameter parabolic antennas separated by 1.4m each other are used for transmitting and receiving the wave. The direction of the antennas is fixed at the zenith. The radar is designed to observe clouds between 0.3 and 15 km in height with a resolution of 15 m. The system was integrated and sensitivities and stabilities have been measured. Results of test measurements of clouds show that the system is sensitive and stable enough to observe various clouds.

Many studies on soil moisture retrieval at vegetated area from microwave radiometry data assume a simple model of vegetation, which is characterized by vegetation volume fraction, effective dielectric constant, plant moisture content, etc. In this study, a radiative transfer model is used to model the emissivity and transmissivity of forest canopy, which is more realistically characterized as a volumetric medium consisting of discrete scatters (leaves, stems, tree branches, and trunks). To facilitate the soil moisture inversion from radiometry data, the unknown variables need to be reduced. The possibility of fitting the modeled emissivity and transmissivity of vegetation canopy into simple equations, and the relationships of these parameters between different microwave radiometry frequencies were studied and the results are presented in this paper.

Revised versions of previous passive microwave land rainfall algorithms are developed for the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI), the Special Sensor Microwave/Imager (SSM/I), and the new Advanced Microwave Sounding Radiometer-Earth Observing System (EOS) (AMSR-E). The relationships between rainfall rate and 85 GHz brightness temperature are re-calibrated with respect to previous algorithms using collocated TMI and TRMM Precipitation Radar (PR) data. Another new feature is a procedure to estimate the probability of convective rainfall, as convective/stratiform classification can reduce the abmiguity of possible rainfall rates for a given brightness temperature. These modifications essentially eliminate the global high bias found in studies of previous versions of the SSM/I and TMI algorithms. However, many regional and seasonal biases still exist, and these are identified. The applicability of the new features to the other microwave sensors is studied using SSM/I data. The AMSR-E algorithm is the same as the TMI, as the footprint resolutions and frequencies of these instruments are very similar. The TMI algorithm will be used in the land portion of the offical Version 6 TMI instantaneous rainfall rate product, to be released in 2003, while the AMSR algorithm will be used for future AMSR-E products.

The planned Global Precipitation Mission (GPM) consists of a core satellite carrying a state-of-the-art dual frequency precipitation radar and a passive microwave radiometer. In addition, the GPM concept uses a constellation of satellites carrying passive microwave radiometers in order to achieve three hourly rainfall sampling. This constellation consists of radiometers on operational satellites such as the current SSM/I series, as well as some that are planned specifically as part of the GPM mission. As such, GPM is both a satellite "mission", as well as a concept designed to combine the many international assets into a coherent framework. In order to achieve this conceptual benefit, however, it is imperative that we develop algorithms and error models that allow a coherent description of rainfall to emerge from wide ranging sets of sensor capabilities. This paper will discuss work being performed to develop such a framework for the algorithms.

A scheme of Integrative Return Signal Simulator (IRSS) of satellite altimeter and scatterometer is designed. The IRSS realized by the scheme can compensate the insufficiency of general measure and calibration equipment. It can calibrate easily the transfer function of satellite altimeter and scatterometer and test the property of software and hardware in laboratory and launching site. The IRSS is based on the principle of the pulse regeneration and the return signal is synthesized at digital method. The scheme can realize modulation of pulse width and Doppler frequency shift for return signal. The problems such as accurate delay of radio-frequency and the phase difference shift and so on can be resolved. The simulation results show that the synthesized return signal not only may satisfy fully the statistical law of actual return signal but also can include information concerned for testing the property of altimeter and scatterometer. The method lays a reference for developing other type of Return Signal Simulator (RSS) in future.

A revised geophysical model function for applications of QuikSCAT data to tropical cyclones is described. An analysis of QuikSCAT σ₀s from the fore- and aft-beams indicates a directional dependence of about 0.5-1 dB for above 40 m/s wind speeds. The differences between QuikSCAT fore- and aft-beam σ₀s were used to estimate the second harmonics coefficients, characterizing the upwind and crosswind asymmetry. The results show that the QuikSCAT σ₀s have a peak-to-peak wind direction modulation of ~1 dB at 35 m/s wind speed, and the amplitude of modulation decreases with wind speed. The trend agrees well with the QSCAT1 model function at near 20 m/s. A simple analytic correction of the QSCAT1 model function is presented. We explored two microwave radiative transfer models to account for the attenuation and scattering effects of rain. One is derived from the collocated QuikSCAT and SSM/I data set, and the other one is a published parametric model developed for precipitation radars. The comparison of these two radiative transfer models indicates the relative significance of volume scattering, scattering from rain-roughened surfaces and rain attenuation. The models suggest that the σ₀s of wind-induced surfaces at 40-50 m/s are comparable to the contributions of rain for up to 10-20 mm/h. The radiative transfer models have been used to retrieve the ocean wind vectors from the collocated QuikSCAT and SSM/I rain rate data for several tropical cyclones. The resulting wind speed estimates of these tropical cyclones show improved agreement with the expected wind fields derived from the best track analysis and Holland's model for up to about 15 mm/h rain rate.

In this paper an in-depth analysis on the performance of the Fourier analysis in estimating the first moment of Doppler spectra of rain signals from a spaceborne radar is presented. Spectral moments estimators based on Fourier analysis (DFT-SME) have been widely used by Doppler weather radars in measuring rainfall velocity and they have been found to be almost optimal for narrow normalized spectral widths (w_N). They are also more computationally efficient than the Maximum Likelihood estimators. However, the existing analytical approaches for evaluating the DFT-SME performance have mostly been focused on a limited range of small w_N (e.g., w_N< 0.1) that are typical of ground based and airborne Doppler weather radars. With the rapid advances in spaceborne radar technologies, the flying of a Doppler precipitation radar in space to acquire global data sets of vertical rainfall velocity has become a real possibility. The objective of this work is to develop a generalized analytical approach by extending it to cover larger values of w_N (e.g., w_N ~ 0.2) in spaceborne radar applications. In particular, a method has been developed to properly treat the aliasing effects, which have become a significant error source in spaceborne applications. Several DFT-SME algorithms (differing in the adopted strategy for noise handling and the initial estimate of the mean Doppler velocity) have been analyzed with this generalized approach. The analytical results are in excellent agreement with those obtained through simulation. Such encouraging results suggest that the proposed approach is a reliable technique for fast and accurate prediction of DFT-SME performance for spaceborne Doppler weather radars.

In this paper we present a sea surface radar echo spectral analysis technique to correct for the rainfall velocity error caused by radar pointing uncertainty. The correction procedure is quite straightforward when the radar is observing a homogeneous rainfall field. On the other hand, when NUBF occurs and attenuating frequencies are used, additional steps are necessary in order to correctly estimate the antenna pointing direction. This new technique relies on the application of Combined Frequency-Time (CFT) algorithm to correct for uneven attenuation effects on the observed sea surface Doppler spectrum. The performance of this correction technique was evaluated by Monte Carlo simulation of the Doppler precipitation radar backscatter model, and the high-resolution 3D rain fields generated by a cloud resolving numerical model. Our preliminary results show that the antenna pointing induced error can indeed be successfully removed by the proposed technique.

The research of the X-Band High Spatial Resolution Radiometer (X- band HSRPR) is trying to enhance the spatial resolution by the synthetic aperture antenna. Its sensitive of the temperature measurement is 1.5K and the spatial resolution is 2°. The receiver includes 8 channels. Every channel compose 4 main parts: (1) high frequency components, (2) mid frequency amplifiers, (3) power dividers, (4) correlation detectors, and 2 supplementary parts: (1) local oscillator and (2) noise source. The article introduces the designing of the receiver and the characters designing of the components in the channels.

The IF simulator is an important equipment to test and calibrate spaceborne radar altimeter before launch. In this paper, a new IF simulation algorithm (IFSA) is introduced, based on which an IF simulator is developed successfully. The IF simulator is designed to provide simulated I/Q signals and related interface, so that the tracker of altimeter can be tested and calibrated in close-loop under the simulated circumstance. That means the IF simulator should have the ability to simulate the variety of the distance from the satellite to the sea surface, the significant wave height (SWH), the backscatter coefficient of the sea surface, the sea scattering noise, and the thermal noise of the radar altimeter electronic system. Besides IF simulation, IFSA can be used in RF simulator, and it has been proved to be correct and useful by lots of experiments. The IF simulator includes the monitor unit, the data processing unit, timing unit, D/A unit, buffer unit, and interface unit.

In this paper, the effects from mutual coupling and imbalance between channels on the interferometric correlation are analyzed. It is shown that the correlation error is mainly introduced by the phase imbalance. The phase balance of the central frequency can ensure the accuracy of the correlation phase, and the residual phase error will only reduce the coherency. Coherent/Incoherent noise calibration can correct the channel imbalance related interferometric correlation errors.

Soil moisture is an important parameter for hydrological and climatic investigations. Future satellite missions with L-band passive microwave radiometers will significantly increase the capability of monitoring Earth's soil moisture globally. Understanding the effects of surface roughness on microwave emission and developing quantitative bare surface soil moisture retrieval algorithms is one of the essential components in many applications of geophysical properties in the complex earth terrain by microwave remote sensing. In this study, we explore the use of the Integral Equation Model (IEM) for modeling microwave emission. This model was validated using a three-dimensional Monte Carlo model. The results indicate that the IEM model can be used to simulate the surface emission quite well for a wide range of surface roughness conditions with high confidence. Several important characteristics of the effects of surface roughness on radiometer emission signals at L-band 1.4 GHz that have not been adequately addressed in the current semi-empirical surface effective reflectivity models are demonstrated by using IEM simulated data. Using an IEM simulated database for a wide range of surface soil moisture and roughness properties, we developed a parameterized surface effective reflectivity model with three typically used correlation functions.

A new algorithm is developed to retrieve the land surface emissivity at four SSM/I frequencies. The coefficients used in the algorithm are directly derived from a training data set including land emissivity and SSM/I measured brightness temperatures. The training data set is also derived from SSM/I brightness temperatures by removing the effects of atmospheric emission and surface temperature. In doing so, the upwelling and downwelling brightness temperature as well as atmospheric transmittance are computed directly from the global data assimilation system (GDAS) temperature and moisture profiles, and surface temperature. Global monthly mean emissivity maps are generated from the daily products. The retrievals under rain regions are not used in the averaging. The rainy pixels were screened out using SSM/I precipitation algorithm.

Microwave refractometer, a scientific instrument for the determination of atmospheric refractivity which is a function of air pressure, the temperature and the water vapor pressure, has much more exactness and faster response than the sensors of the pressure, the temperature and the humidity. A theory based on the parameter pseudo-refractivity has been developed, which can be used for the more exact determination of flux relationships with microwave refractometer. In this paper the advantages of microwave refractometer over the sensors are analyzed.

Polarimetric radar has been successful in characterizing cloud and precipitation. Polarization parameters such as radar reflectivity (Z), differential reflectivity (Z_DR), linear reflectivity difference (Z_DP), and specific differential propagation phase shift (K_DP) provide more information about precipitation and allow better characterization of hydrometeors, accurate rain rate estimation, and retrieval of rain drop size distribution (DSD). In the past, rain rate (R) estimation from S-Pol was based on empirical models such as Z-R, R (Z, Z_DR) and R (K_DP) relations, which were derived from regression analyses of radar and rain gauge measurements or numerical simulations, and are prone to errors. In this paper, we derive various rain estimators using a physically-based raindrop size distribution (DSD) model: constrained Gamma DSD. The constrain condition, the shape-slope relation in Gamma DSD, is derived from video-disdrometer observations and has been shown to represent a physical nature of rain and useful in rain DSD retrievals from remote measurements. The relation is then used to retrieve rain DSDs from two remote measurements and t derive rain estimators from polarimetric radar measurements. The derived rain estimators are then used to retrieve rain parameters from S-pol measurements. The results are also verified and compared with ground measurements.

The relationship between attenuation coefficient, k, and radar reflectivity factor, Z, as well as that between Z and rainfall rate, I, is influenced by the shape of precipitating raindrops and the orientation of their rotatory axes respect to the polarization direction of the incident radar wave. Provided that the orientation of rotatory axes of poly-disperse small spheroid particles is known, correctable thickness of radar echo for attenuation depends mainly on rainfall rate. The heavier the rainfall rate, the thinner the correctable thickness. For an 80mm/hour precipitation uniformly distributed along a radial direction, correctable thickness of radar echo is more than 120km for 5.6cm wavelength and about 50km for 3.2cm if correction algorithm R2 or R3 is used. Orientation information is critically important during attenuation correction. Right k-Z relationship must be coupled with right orientation status.

In order to enhance the sensing power of precipitating clouds, a dual-polarization radar is developed to be added in an active and passive microwave dual-wavelength (X/Ka band) remote sensing system. After this upgrading of dual-polarization function addition, the advanced properties of the principle of the former remote sensing system are augmented. This upgraded system becomes a new capability of synthetically sensing clouds and precipitation, and will play an important role in precipitating cloud structure studies and all kinds of major science projects concerning obtaining quantitative distribution of clouds and precipitation. The working principle, the upgrading method and the specifications of the new system and its main components are given here. An emphasis is laid on the description of the design and implementing means of the antenna-feeding unit, the control unit and data acquisition unit. A preliminary rainfall observation test of the system is also presented in this paper.

The U.S. NOAA Profiler Network operated by the Forecast Systems Laboratory for more than a decade represents the culmination of several decades of research and development of wind profiling Doppler radars. The NOAA Profiler Network is comprised of 35 tropospheric wind profilers (404/449 MHz) mostly located in the central United States. The infrastructure, built over the years for the NOAA Profiler Network has the flexibility and capacity to handle many other profilers in addition to the 35 NOAA Profiler Network systems. With recent advances in computers, networking and communication technologies, real-time profiler data can be acquired from almost anywhere on the globe. Data from remote sites are submitted to quality control and placed onto the Global Telecommunication System. Currently the Forecast Systems Laboratory is receiving data from about 80 sites in the continental U.S., Alaska, Canada, and along the equator west from South America. The data are routed to operational forecast centers where the data are used in a variety of numerical weather prediction models and also distributed to the local forecast offices to tailor model guidance to local conditions. The data are also placed on the Forecast Systems Laboratory web site http://www.profiler.noaa.gov. Here the data may be viewed in many graphical forms and are also available for downloading to a user’s site in numeric format.

It has been studied that the shift of polarization orientation angle ψ at the maximum of co-polarized or cross-polarized back-scattering signature can be converted to the surface slopes. It is then utilized to generate the digital elevation mapping (DEM) and terrain topography using two-pass fully polarimetric SAR or interferometric SAR (INSAR) image data. This paper, using the Mueller matrix solution, newly derives the ψ shift as a function of three Stokes parameters, I_vs, I_hs, U_s, which are measured by polarimetric SAR image data. Using the Euler angles transformation, the orientation angle ψ is related to both the range and azimuth angles of the tilted surface and radar viewing geometry. When only a single-pass SAR data is available, the adaptive thresholding method and image morphological thinning algorithm for linear textures are proposed to first determine the azimuth angle. Then, making use of full multi-grid algorithm, both the range and azimuth angles are utilized to solve the Poisson equation of DEM to produce the terrain topography.

The Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) is a passive microwave radiometer on board the National Aeronautics Space Agency (NASA) Earth Observing System (EOS) Aqua satellite. Aqua, a sun-synchronous polar orbiting platform, is one of a series of spacecraft launched by NASA's Earth Science Enterprise to obtain remotely-sensed data for the advancement of Earth System Science. The AMSR-E measures passive microwave radiation, allowing for derivation of many parameters, including soil moisture, sea surface temperatures, rain rate, snow cover and sea ice extent. The instrument provides improved spatial resolution compared to earlier generations of spacecraft-borne passive microwave instruments (e.g., SMMR and SSM/I) and retrieves information in more frequencies of the microwave spectrum than its predecessors. Data products are archived and distributed by the National Snow and Ice Data Center (NSIDC) Distributed Active Archive Center (DAAC) at the University of Colorado, Boulder (www.nsidc.org/daac/amsr/). This paper summarizes the AMSR-E data sets that will be available at NSIDC and discusses research applications of some of them.

Snow is an important fresh-water resource in China especially in west northern China. This paper presents a review of studying on snow cover distribution and snowmelt runoff simulating and forecasting using remote sensing date sets in China. The snow cover mapped derived from different methods and hillshading effect will be discussed in detail. A dominating model-SRM (Snowmelt Runoff Model) model and some statistics models have been used to simulating the discharge in different watersheds. Meanwhile, based on the fact of climatic change, a sample in Heihe Basin in West northern China has been given to prove that the snowmelt runoff regime has relevantly changed in the time and spatial. Also, some viewpoints are given voice to research on snowmelt runoff modeling at the present time and further.

The estimation of snow parameters such as snow extent, snow depth and snow water equivalent are very important. They are parameters in land surface schemes and are very useful in snow disaster assessment. Passive microwave remote sensing has advantages in retrieving these parameters, especially snow depth. However, this technique has not been applied to monitor snow in Tibetan Plateau so far. So since last winter we tried to operationally monitor snow in this area by using SSM/I data, providing daily snow depth maps to the concerning sections of local government. In the meantime, the in-situ measurements of snow depth data in the Tibetan Plateau were collected to validate the retrieval algorithm employed in this study. In the paper, SSM/I images before and after a heavy snowfall were analyzed and compared with MODIS images. The results showed that the snow extent from SSM/I data is consistent with that from MODIS data, and snow depths from SSM/I are helpful for the assessment of snow disaster. However, compared with in-situ observations SSM/I derived snow depths are significantly overestimated. Since passive microwave remote sensing is almost transparently to atmosphere and cloud, it will play an important role in monitoring snow in the Tibetan Plateau, wih the retreival algorithm being improved. This will be more dominant when AMSR data are available.

Echo tracking is always an important part in the design of radar altimeter, which is required in many ways. But above all, the essential requirement for the tracking is to avoid track losing, furthermore, the tracking precision and algorithm operation scale also are need to be optimized. So all the factors mentioned above have to be considered comprehensively. Till today, various tracking algorithms have been developed for different applications, but the common shortage of them is that they depend on some specific target modes strongly, which leads to poor adaptability to different targets and easily losing tracking. As to this problem, OCOG will be introduced in this paper, which is a good robust tracking algorithm and will be applied into a new type of satellite altimeter, tridimensional imaging radar altimeter, which will be applied in complex environments including sea, seaice and coast.