Precipitation profiling from vertically-looking ground-based radar profilers operating at frequencies of 915- and 2835-MHz have been demonstrated to be useful tools in several field campaigns during the past decade. When combined with a surface disdrometer and a nearby scanning radar, the calibrated profiling radar provides high resolution details of the precipitation vertical structure while the scanning radar provides the horizontal context of the precipitation relative to the profiler site. Profiling radars provide detailed information of reflectivities and drop-size distributions that are essential for quantitative precipitation estimation (QPE). One role that profiling radars have in QPE is monitoring the calibration of the scanning radar reflectivity used to map the precipitation over a large area. The concept of up-scaling uses a surface disdrometer to calibrate the profiling radar which is then used to calibrate the scanning radar. This method of up-scaling the reflectivities observed by the surface disdrometer to the scanning radar reflectivities eliminates some of the uncertainties of Z-R relationships inherent in surface rain gauge to scanning radar calibrating and monitoring techniques.

Remote detection of cloud phase in either liquid, ice or mixed form a key microphysical observation. Evolution of a cloud system and associated radiative properties depend on microphysical characteristics. Polarization radars rely on the shape of the particle to delineate the regions of liquid and ice. For specified transmitter and receiver characteristics, it is easier to detect a high concentrations of larger atmospheric particles than a low concentration of small particles. However, the radar cross-section of a given hydrometeor increases as the transmit frequency of the radar increases. Thus, in spite of a low transmit power, the sensitivity of a millimeter-wave radar might be better than high powered centimeter-wave radars. Also, ground clutter echoes and receiver system noise powers are sensitive functions of radar transmit frequency. For example, ground clutter in centimeter-wave radar sample volumes might mask non-precipitating or lightly precipitating clouds. An optimal clutter filter or signal processing technique can be used to suppress clutter masking its effects and/or enhanced weak cloud echoes that have significantly different Doppler characteristics than stationary ground targets. In practice, it is imperative to investigate the actual performance of S and Ka-band radar systems to detect small-scale, weak cloud reflectivity. This paper describes radar characteristics and the sensitivity of the new system in non-precipitating conditions. Recently, a dual-wavelength S and Ka-band radar system with matched resolution volume and sensitivity was built to remotely detect supercooled liquid droplets. The detection of liquid water content was based on the fact that the shorter of the two wavelengths is more strongly attenuated by liquid water. The radar system was deployed during the Winter Icing Storms Project 2004 (WISP04) near Boulder, Colorado to detect and estimate liquid water content. Observations by dual-wavelength radar were collected in both non-precipitating and lightly precipitating clouds.

Following the successful Precipitation Radar (PR) of the Tropical Rainfall Measuring Mission1, a new airborne, 14/35 GHz rain profiling radar, known as Airborne Precipitation Radar - 2 (APR-2)2, has been developed as a prototype for an advanced, dual-frequency spaceborne radar for a future spaceborne precipitation measurement mission3. This airborne instrument is capable of making simultaneous measurements of rainfall parameters, including co-pol and cross-pol rain reflectivities and vertical Doppler velocities, at 14 and 35 GHz. Furthermore, it also features several advanced technologies for performance improvement, including real-time data processing, low-sidelobe dual-frequency pulse compression, and dual-frequency scanning antenna. Since August 2001, APR-2 has been deployed on the NASA P3 and DC8 aircrafts in four experiments including CAMEX-4 and the Wakasa Bay Experiment. Raw radar data are first processed to obtain reflectivity, LDR (linear depolarization ratio), and Doppler velocity measurements. The dataset is then processed iteratively to accurately estimate the true aircraft navigation parameters and to classify the surface return. These intermediate products are then used to refine reflectivity and LDR calibrations (by analyzing clear air ocean surface returns), and to correct Doppler measurements for the aircraft motion. Finally, the melting layer of precipitation is detected and its boundaries and characteristics are identified at the APR-2 range resolution of 30m. The resulting 3D dataset will be used for validation of other airborne and spaceborne instruments, development of multiparametric rain/snow retrieval algorithms and melting layer characterization and statistics. In this paper the processing approach is described in detail together with an overview of the resulting data quality and known issues.

A new, fast radiative transfer model including scattering has been developed for the purpose of microwave radiance assimilation in cloudy and precipitating areas. The model uses a technique called successive order of interaction (SOI) which is based on a blending of the doubling and the successive order of scattering techniques. An adjoint and tangent linear version of the model are also available. Within this paper we present first applications of the SOI model. We compare brightness temperatures simulated from NCEP's Global Forecasting System (GFS) using a non-scattering version of the SOI model with global satellite data obtained by the Advanced Scanning Microwave Radiometer (AMSR-E) onboard NASA's Aqua spacecraft. Additionally, we show first sensitivity studies using the adjoint model for cases that include scattering by liquid and frozen precipitation.

Hurricanes, blizzards and other weather events are important to understand not only for disaster preparation, but also to track the global energy balance and to improve weather and climate forecasts. For several decades, passive radiometers and active radars on aircraft and satellites have been employed to remotely sense rain rates and the properties of liquid particles. In the past few years the relationships between frozen particles and millimeter-wave observations have become understood well enough to estimate the properties of ice in clouds. In this paper, a brief background of passive remote sensing of precipitation will be presented followed by a focused discussion of recent research at NASA Goddard Space Flight Center estimating the properties of frozen particles in clouds. The retrievals are for (1) ice that will eventually melt into rain, (2) for solid precipitation falling in northern climates, and (3) cirrus ice clouds. The electromagnetic absorption and scattering properties and differences of liquid rain versus frozen particles will be summarized for frequencies from 6 to 340+ GHz. Challenges of this work including surface emissivity variability, non-linear and under-constrained relationships, and frozen particle unknowns will be discussed. Retrieved cloud particle contents and size distributions for ice above the melting layer in hurricanes, retrieved snowfall rates for a blizzard, and cirrus ice estimates will be presented. Future directions of this work will also be described.

An algorithm to retrieve the optically thick ice cloud microphysical property profiles is developed by using the GSFC 9.6 GHz ER-2 Doppler Radar (EDOP) and the 94 GHz Cloud Radar System (CRS) measurements aboard the high-altitude ER-2 aircraft. In situ size distribution and total water content data from the CRYSTAL-FACE field campaign are used for algorithm development. To reduce uncertainty in calculated radar reflectivity factors (Ze) at these wavelengths, coincident radar measurements and size distribution data are used to guide the selection of mass-length relationship and to deal with the density and non-spherical effects of ice crystals on Ze. The algorithm is able to retrieve microphysical property profiles of optically thick ice clouds, such as, deep convective and anvil clouds, which are very challenge for single frequency radar and lidar. An example of retrieved microphysical properties of a deep convective cloud is presented.

Physical approaches in microwave remote sensing to measure non-spherical frozen hydrometeors are frequently parameterized as spherical particles, assuming either dielectric mixing approximation or equivalent spheres, to make Mie-theory applicable. However, the applicability of those simplified approximations in millimeter-wave radar and radiometric remote sensing of frozen hydrometeors need to be evaluated. This study evaluates those fast approximations by comparing with single scattering parameters calculated by the Discrete Dipole Approximation (DDA) method.

Researchers at the National Oceanic and Atmospheric Administration (NOAA), National Environmental Satellite, Data and Information Service (NESDIS) have been at the forefront in the development of precipitation retrieval algorithms from passive microwave sensors for over 20 years. This includes algorithms used for the DMSP Special Sensor Microwave Imager (SSM/I), the TRMM Microwave Imager (TMI), the NOAA Advanced Microwave Sounding Unit (AMSU) and the EOS Aqua Advanced Scanning Microwave Radiometer (AMSR-E). NOAA requires such retrievals to support two of its main scientific mission elements: "Weather and Water" and "Climate Monitoring and Prediction". This presents an overview of the algorithm development, recent advances (i.e., the expansion of the retrievals to over snow covered surfaces) and future plans (i.e., the development of a general retrieval framework adaptable for use with any microwave sensor) as NOAA enters into the National Polar-orbiting Operational Environmental Satellite System (NPOESS) and Global Precipitation Measurement (GPM) era. Application examples to highlight the use of these products at NOAA are also presented.

In an effort to evaluate scattering models for particle size distributions of ice crystals within cirrus clouds, simultaneous data was collected in March 2000 during the Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Cloud Intensive operational period (Cloud IOP) at the Cloud and Radiation Testbed (CART) site in Lamont, Oklahoma. In situ measurements of ice particles were collected using the National Center for Atmospheric Research (NCAR) Video Ice Particle Sampler (VIPS), which flew on the University of North Dakota Citation research aircraft. Ground-based vertical radar profiles were collected using the University of Massachusetts (UMass) 33GHz/95GHz Cloud Profiler Radar System (CPRS). Data from both sensors was used to retrieve and compare the equivalent radar reflectivity at Ka band (33GHz). The equivalent radar reflectivity measured by the ground-based, zenith-looking, CPRS radar at Ka band and compared to the reflectivity computed from the airborne VIPS samples of particle size distribution, N(D), using Mie theory. As anticipated the equivalent reflectivity of the radar and VIPS were similar at the time the UND Citation overflew the radar.

The Microwave Sounding Units (MSU) aboard the NOAA series of polar orbiting satellites has been used to monitor the very small trend in the global tropospheric temperature over the 25 year satellite record. To obtain a homogeneous data set, calibration corrections were made to each of the nine MSU's in the form of fixed biases, and in some cases temperature-dependent adjustments, using data during the overlap periods. Up until now, however, the adjustments are empirically based. To improve the accuracy, this paper develops a calibration model that includes errors in the cold space and warm target measurements, as well as the nonlinear factor. Corrections for these calibration errors are estimated using a least squares minimization where the predictors are the differences between all twelve overlapping satellite measurements. After applying the calibration corrections, the globally averaged differences between satellite instruments are no larger than 0.03 K. It is also found that the globally averaged tropospheric temperature trend obtained from MSU channel 2 measurements is 0.17 K/decade, which is nearly the same as the surface trend.

One-dimensional variational (1DVAR) retrievals of humidity profiles over ocean are generated from AMSU-B observations and a Navy Operational Global Atmospheric Predication System (NOGAPS) background. Retrievals of water vapor profiles from AMSU-B are validated by intercomparing GOES water vapor channel observations with simulated GOES brightness temperatures calculated by the RTTOV-7 forward model from the background and retrieved atmospheric profiles. Brightness temperatures simulated from the retrieved profiles matched the GOES observations much more closely than those calculated from the background profile. After screening out clouds, GOES Imager 6.7-mm brightness temperatures from GOES-10 were reproduced with a correlation of 0.94, a bias of 2.97 K, and a standard deviation of -1.60 K. The impact of AMSU-B retrievals assimilated using NAVDAS is tested by comparing two data assimilation runs. 500-mb and 300-mb specific humidity fields are compared to GOES imagery. Large-scale moisture features such as the Intertropical Convergence Zone (ITCZ) and South Pacific Convergence Zone (SPCZ) at 500 mb are sharper and better defined in the AMSU-B run, agreeing qualitatively with GOES observations. However, differences in simulated GOES water vapor channel brightness temperatures from the two model runs are very small, due to the limited impact of amsu-b in the upper troposphere in this run.

Backscattering enhancement from random hydrometeors should increase as wavelengths of radars reach millimeter regions. For 95 GHz radars, the reflectivity of backscattering is expected to increase by 2 dB, due to multiple scattering including backscattering enhancement, for water droplets of diameter of 1 mm with a density of 5 x 10³ m^-3. Previous theoretical studies of backscattering enhancement considered infinitely extending plane waves. In this paper, we expand the theory to spherical waves with a Gaussian antenna pattern, including depolarizing effects. While the differences from the plane wave results are not great when the optical thickness is small, as the latter increases the differences become significant, and essentially depend on the ratio of radar footprint radius to the mean free path of hydrometeors. In this regime, for a radar footprint that is smaller than the mean free path, the backscattering-enhancement reflectivity corresponding to spherical waves is significantly less pronounced than in the case of the plane wave theory. Hence this reduction factor must be taken into account when analyzing radar reflectivity factors for use in remote sensing applications.

The ability of the Tropical Rainfall Measuring Mission Microwave Imager (TRMM/TMI) for remote sensing of cloud liquid water (CLW) has been demonstrated in this study. Due to the great sensitivity of the TMI 85.5 GHz channels to CLW, the CLW for non-precipitating clouds over land can be successfully estimated with the measurement data of TMI 85.5 GHz channels using the Vector DIScrete Ordinate Radiative Transfer (VDISORT) model based on the iteration steps. A numerical step-by-step method is proposed for the microwave surface emissivity estimate over land with the help of VDISORT model. The brightness temperature at 85.5 GHz in vertical polarization (TB85V) of TMI was further applied for the CLW estimates over land regions during the Huaihe River Basin Energy and Water Cycle Experiment (HUBEX) in China. The retrieval results based on TRMM/TMI data show reasonable agreement with the ground-based microwave radiometer measurements. It is proved that the physical method presented in this study for the CLW retrieval over land is feasible and valid.

Salinity is important for understanding ocean dynamics, energy exchange with the atmosphere and the global water cycle. Existing data is limited and much of the ocean has never even been sampled. Sea surface salinity can be measured remotely by satellite and a three year mission for this purpose called Aquarius/SAC-D has recently been selected by NASA's Earth System Science Pathfinder (ESSP) program. The objective is to map the salinity field globally with a spatial resolution of 100 km and a monthly average accuracy of 0.2 psu. The mission, scheduled for launch in 2008, is a partnership of the United States National Aeronauatics and Space Agency (NASA) and the Argentine Comision Nacional de Actividades Epaciales (CONAE).

The National Oceanic and Atmospheric Administration (NOAA) is considering a microwave radiometer for the next series of Geostationary Operational Environmental Satellites (GOES-R) to be launched starting in 2012. This paper examines the products proposed for the geostationary microwave radiometer in the light of current microwave retrieval algorithms and estimates the performance achievable from geostationary altitude with a three-meter antenna. The results suggest that hemispheric soundings and rain rates can be generated on an hourly basis with the desired accuracy and horizontal resolution, that capping inversions can be detected in conjunction with infrared soundings, that hurricane warm core temperatures can be resolved using high frequencies plus deconvolution and that ocean wind and total precipitable water products can be provided with close to the desired resolution.

Knowledge of the global distribution of the vertical velocity of precipitation is important in the study of energy transportation in the atmosphere, the climate and weather. Such knowledge can only be directly acquired with the use of spaceborne Doppler precipitation radars (DPR). Although the high relative speed of the radar with respect to the rainfall particles introduces significant broadening in the Doppler spectrum, recent studies have shown that the average vertical velocity can be measured to acceptable accuracy levels by appropriate selection of radar parameters. Furthermore, methods to correct for specific errors arising from non-uniform beam filling (NUBF) effects and pointing uncertainties have recently been developed. In this paper we will present the results of the trade studies on the performances of a spaceborne Doppler radar with different system parameters configurations. Particular emphases will be placed on the choices of: 1) the PRF vs. antenna size ratio, 2) the observational strategy, 3) the operating frequency; and 4) processing strategy. The results show that accuracies of 1 m/s or better can be achieved with the currently available technology.

In hydrological investigations, modeling and forecasting of snow melt runoff requires timely information about snow properties and their spatial variability. The liquid water content in snow pack is an important parameter. Previous study1 has indicated that the fully polarimetric C-band synthetic aperture radar (SAR) is capable to estimate the free liquid water content-snow wetness-in the top layer of a snow pack quantitatively. The objective of this study is to evaluate the capability of a radar system with measurements of the dual frequency (C-band 5.3 GHz and Ku-band 13.4 GHz) and of the dual-polarization (VV and HV) in estimation of snow wetness based on the numerical simulation. We have established C-band and Ku-band radar wet snow data-base by using second-order radiative transfer backscattering model. The data-base covers the most possible wet snow physical properties and surface roughness conditions. Using this data-base, an inversion algorithm has been developed for snow wetness retrieval. The newly developed algorithm mainly involved two steps: 1) decomposing the surface and volume scattering signals using depolarization factor, and 2) using each scattering component (surface or volume backscattering signals) to estimate snow wetness.

In this paper, we evaluate the capability of a multi-scattering microwave emission model that including the Dense Media Radiative Transfer Model (DMRT) and AIEM to simulation of dry snow emission with Matrix Doubling approach. We compared the predictions of this model with the ground experimental measurements. The comparison showed that our snow microwave emission model agreed well with the experimental measurements. In order to develop retrieval snow properties: snow depth or snow water equivalence (SWE) retrieval algorithm, we carried out the sensitivity test between the emission models with the different scattering-order: the zeroth-order, the first-order and the multi-scattering models. The results indicated that the multi-scattering effects have to be taken into account in the snow emission model, especially for large grain size. Due to the complexity of the multi-scattering model, we developed a parameterized inversion model using our multi-scattering emission model with a wide range of snow and under-ground properties for algorithm development purpose.

Landscape transitions between seasonally frozen and thawed conditions occur each year over roughly 50 million square kilometers of Earth's Northern Hemisphere. These realtively abrupt transitions represent the closest analog to a biospheric and hydrologic on/off switch existing in nature, affecting surface meteorological conditions, ecological trace gas dynamics, energy exchange and hydrologic activity profoundly. We utilize time series satellite-borne microwave remote sensing measurements from the Special Sensor Microwave Imager (SSM/I) to examine spatial and temporal variability in seasonal freeze/thaw cycles for the pan-Arctic basin and Alaska. Regional measurements of spring thaw timing are derived using daily brightness temperature measurements from the 19 GHz, horizontally polarized channel, spearately for overpasses with 6 AM and 6 PM equatorial crossing times. Spatial and temporal patterns in regional freeze/thaw dynamics show distinct differences between North Americ and Eurasia, and boreal forest and Arctic tundra biomes. Annual anomalies in the timing of thawing in spring also correspond closely to seasonal atmospheric CO₂ concentration anomalies derived from NOAA CMDL arctic and subarctic monitoring stations. Classification differences between AM and PM overpass data average approximately 5 days for the region, through both appear to be effective surrogates for monitoring annual growing seasons at high latitudes.

This study focuses on microwave land surface emissivity estimation over Northern Africa and the Middle East and the related impact on temperature and moisture retrievals. Land surface temperature retrievals are performed using a plane-parallel radiative transfer model, analyses from the Navy Operational Global Atmospheric Prediction System (NOGAPS) and data from the High Resolution Infrared Radiation Sounder Version 3 (HIRS/3). Infrared surface emissivity is indexed to each location using soil and vegetation databases provided by the Global Land Data Assimilation System (GLDAS), and spectral reflectance libraries of soil and vegetation. Initial microwave land emissivity estimates are made using a plane-parallel radiative transfer model, the infrared retrieved land surface temperatures, analyses from NOGAPS, and data from the Advanced Microwave Sounding Unit (AMSU). Perturbations of the atmospheric profiles and land surface temperatures provide estimates of the microwave emissivity error covariances necessary for retrievals and radiance assimilation. The error estimation is used in both the Naval Research Laboratory (NRL) 1DVAR retrieval, and for future use in 3DVAR radiance assimilation with the NRL Atmospheric Variational Data Assimilation System (NAVDAS). The window channels on AMSU/A have shown sensitivity to both temperature and moisture in the lowest five kilometers of the atmospheric profile, with these sensitivities strongly correlated to the estimate of the microwave land emissivity. Though the sensitivities are strongly correlated in the vertical dimension, an ability to extract meaningful profiling information from the microwave data is displayed. Further, the atmospheric sensitivity is linked to the precision to which the microwave radiances are estimated.

This paper reports an attempt in improving surface soil moisture radar algorithm for Hydrosphere State Mission (Hydros). We used a Radiative Transfer Model to simulate a wide range surface dielectric, roughness, vegetation with random orientated disks database for our algorithm development under HYDROS radar sensor (L-band multi-polarizations and 40º incidence) configuration. Through analyses of the model simulated database, we developed a technique to estimate surface soil moisture. This technique includes two steps. First, it decomposes the total backscattering signals into two components - the surface scattering components (the bare surface backscattering signals attenuated by the overlaying vegetation layer) and the sum of the direct volume scattering components and surface-volume interaction components at different polarizations. From the model simulated data-base, our decomposition technique works quit well in estimation of the surface scattering components with RMSEs of 0.12, 0.25, and 0.55 dB for VV, HH, and VH polarizations, respectively. Then, we use the decomposed surface backscattering signals to estimate the soil moisture and the combined surface roughness and vegetation attenuation correction factors with all three polarizations. Test of this algorithm using all simulated data showed that an accuracy for the volumetric soil moisture estimation in terms of Root Mean Square Error (RMSE) of 4.6 % could be achievable.

A new differential measurement concept is presented for retrieving the total content of water vapor (Iwv, Integrated water vapor) along the propagation path between two Low Earth Orbiting (LEO) satellites, while such path is immersing in the atmosphere during a so called set occultation. This new approach, referred to as DSA (Differential Spectral Absorption) method, is based on the simultaneous measurement of the total attenuation at two relatively close frequencies in the K band, and on the estimate of a "spectral sensitivity parameter" that is highly correlated to the Iwv content of the LEO-LEO link in the low troposphere. The DSA approach has the potential to overcome all spectrally 'flat' and spectrally correlated phenomena, including atmospheric scintillation, but a very appealing aspect is the aforementioned correlation, on which we focus in this paper, taking into consideration signals at 17 and 20 GHz, and verifying how the correlation between Iwv and spectral sensitivity changes with season, latitude and inhomogeneity of the atmosphere.

The Advanced Technology Microwave Sounder (ATMS) meteorological flight instruments for use on board the NPOESS Preparatory Project (NPP) spacecraft and the National Polar-Orbiting Operational Environmental Satellite System (NPOESS), is a multi-channel microwave radiometer. The ATMS is a total power radiometer system that passively monitors the radiation from the earth's surface and atmosphere in the microwave portion of the spectrum. It is a cross-track, line-scanned instrument designed to measure scene radiance's in twenty two discrete frequency channels. The paper presents instruments predicted performance for first flight unit.

Bright band is perhaps one of the most widely known radar signatures for a long time. Extensive research has been reported in the literature, documenting various features of bright bands. Nevertheless it has always been a challenge to obtain a clear classification of the underlying structure of the vertical profile of reflectivity from radar observations. Classifying the radar profiles of bright band to a limited set will be helpful to further this goal. In this study the Self Organizing Map (SOM) technique is used to classify bright band observed from the Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR). The SOM forms a non-linear mapping of the data to a two-dimensional grid map that can be used as an exploratory analysis tool for generating hypotheses on the shape, and characteristics of the vertical profiles of bright bands. Observation of reflectivity profiles in bright band obtained from the TRMM-PR at 125 m vertical resolution are analyzed and visualized with the SOM. Basic descriptions of the vertical structure are extracted from the TRMM-PR bright band region data, and the corresponding statistics are presented in this paper. Statistics of bright band thickness, peak reflectivity, snow reflectivity, rain reflectivity, and the sharpness of bright bands are studied at a global scale over the tropics. The global classification map provides a unique tool to quantitatively classify bright band region. The methodology and results of the analysis are presented in this paper.

We chose the Kushiro wetland in Hokkaido, Japan, as a test site to monitor wetland areas. Synthetic aperture radar (SAR) can carry out continuous observation in any weather conditions, and can therefore be used to observe high humidity areas such as wetlands. We applied multi-parameter SAR data (dual-frequency, multi-polarization, and multi-incidence angle) to monitoring the wetland forest. To find the optimum incidence angle and polarization for monitoring the wetland biomass, a simple backscattering model of wetland vegetation was developed and applied to estimate backscattering coefficients for different biomass and surface conditions.

The DSA (Differential Spectral Attenuation) approach, presented in a companion paper in this conference's proceedings, has the potential to provide the total content of water vapor (IWV, Integrated Water Vapor) along the propagation path between two Low Earth Orbiting (LEO) satellites. The interest towards the DSA, based on the ratio of simultaneous measurements of the total attenuation at two relatively close frequencies in the K-Ku bands, was moved by the need for limiting the effects of tropopheric scintillation and by the fact that DSA measurements are highly correlated to the IWV along the LEO-LEO link. However, the impact of tropospheric scintillation in a LEO-LEO radio occultation geometry using frequencies above 10 GHz still has to be thoroughly investigated. In this paper we focus on the analysis of such effects, taking into account the fact that the formulations presented in the literature have to be modified in order to fit the specific problem under consideration. Specifically, an expression is derived for the variances of the amplitude and phase fluctuations of the wave, their spectrum and the correlation between fluctuations at different frequencies. In particular, the latter is extremely useful to evaluate the potential of the DSA approach through simulations whose results are reported in the last part of the paper.

Global satellite remote sensing records show evidence of recent vegetation greening and an advance in the onset of the growing season at high latitudes. We apply a terrestrial net primary production (NPP) model driven by satellite observations of vegetation properties and daily surface meteorology from an atmospheric GCM to assess spatial patterns, annual variability, and recent trends in vegetation productivity across Alaska and northwest Canada. We compare these results with regional observations of the timing of growing season onset derived from satellite passive microwave remote sensing measurements from the Special Sensor Microwave Imager, SSM/I. Our results show substantial variability in annual NPP for the region that appears to be driven largely by variations in canopy photosynthetic leaf area and average summer air temperatures. Variability in maximum canopy leaf area and NPP also correspond closely to remote sensing observations of the timing of the primary seasonal thaw event in spring. Relatively early spring thawing appears to enhance NPP, while delays in seasonal thawing and growing season onset reduce annual vegetation productivity. Our results indicate that advances in seasonal thawing and spring and summer warming for the region associated with global change are promoting a general increase in NPP.

The International MODIS (Moderate Resolution Imaging Spectroradiometer) and AIRS (Atmospheric Infrared Sounder) Processing Package (IMAPP) is supported by NASA with the goals of developing a software package which is freely available for processing MODIS and AIRS/AMSU/HSB Data and promoting and supporting the worldwide use of EOS data, and involving the international community in EOS validation efforts. Both NASA's TERRA and AQUA spacecrafts have direct broadcast (DB) X-band downlinks that allow MODIS (on board both TERRA and AQUA) and AIRS/AMSU/HSB and AMSR-E (on board AQUA only) data to be received in real time by sites having the proper reception hardware. In addition to the current released IMAPP, which allows ground stations capable of receiving EOS direct broadcast data to generate products derived from MODIS and AIRS/AMSU/HSB, products from AMSR-E are under developed. Comparison of one month direct broadcast AMSR-E level 1 data with standard AMSR-E level 2A (level 1 data embedded) data archived by MSFC, NASA, have been carried out and the results have shown that the DB level 1 implementation successfully matches the standard products of brightness temperatures in terms of bias, RMS errors and correlation coefficients, i.e., the DB level 1 data are well calibrated and geo-located and are ready for retrieval for geophysical products. The AMSR-E level 2 products for precipitation and soil moisture are currently under evaluation. Those products will be compared with and validated against the official products.