The ability to assimilate microwave radiance observations of the Earth's atmosphere affected by precipitation is investigated with an emphasis on channels that are sensitive to frozen hydrometeors. Mesoscale numerical weather prediction and radiative transfer models, as well as their corresponding adjoint models are utilized in sensitivity and data assimilation experiments. Special Sensor Microwave / Imager observations of Hurricane Bonnie (1998) are compared with model results that are transferred to radiance space with the radiative transfer model. Sensitivity results indicate that the model error in radiance space in areas of precipitation at the initial time is most dependent on the initial hydrometeor fields. At later forecast times, the model error is more sensitive to initial conventional model variables such as water vapor and temperature. When radiance data is assimilated, the model fields have better agreement with the observations compared to a control experiment for all observed channels at the initial time. However, at the next observation time 12 h later, the quantitative error measurements for the control and post assimilation forecasts are approximately the same value. Although this study demonstrates the ability to assimilate observations sensitive to atmospheric ice (as well as liquid) concentrations in a variational framework, important aspects such a background error correlation and bias have been ignored for simplification. More observations in a more complex data assimilation system will be needed in order to fully maximize the forecast impact of these observations.
A physically-sound, non-local excess phase delay observation operator is developed for simulating excess phase delay measurements from GPS radio occultation (RO) missions. By approximating an observed ray by a straight line, the refractivity gradient information along an observed ray path is included in the simulated excess phase delay. This observation operator is used to simulate observations from the German CHAllenging Minisatellite Payload (CHAMP) RO mission based on large-scale analysis. The need to use such an observation operator for GPS RO data assimilation in spherically asymmetric regions is shown by results from a set of forward simulation and data assimilation experiments. A modification that renders the non-local excess phase delay observation operator more suitable for parallel implementation of GPS RO data assimilation is proposed.
Insufficient high vertical and temporal resolution data have limited the precipitation forecast skill of convective initiation. The Geosynchronous Imaging Fourier Transform Spectrometer (GIFTS), a new hyperspectral geostationary satellite measurement system, could provide top-of-atmosphere (TOA) radiances across two broad spectral regions with high-resolutions in spectral, horizontal and temporal spaces within a fixed domain. A set of modeled and observed vertical profiles of atmospheric moisture and temperature during a convective initiation (CI) event within the observing period of the International H2O Project (IHOP_2002) are used to assess the potential values of GIFTS measurements to convective precipitation forecast. First, it is shown that the model simulation captures reasonably well the movement of the precipitation bands and the gradient structures of temperature and water vapor of the convectively initiated storm. Second, the observed vertical and temporal variability of water vapor during the CI period is shown to be quite significant in the lower troposphere. The differences between observations and model simulation are also noticed. Using both the observed and the model-predicted profiles as input to GIFTS radiative transfer model (RTM), it is finally shown that the simulated GIFTS radiance could capture the high vertical and temporal variability of the real and modeled atmosphere prior to the CI, as well as the differences between observations and model forecasts. The study suggests the potential for GIFTS to make important contributions to the improvement of the forecast skill of convective precipitation.
A GPS retrieval algorithm is developed for obtaining in-cloud vertical profiles of the
atmospheric state from Global Positioning System (GPS) radio occultation (RO) data, using
MODIS (Moderate Resolution Imaging Spectroradiometer) cloud-top pressure and cloud-top
temperature as auxiliary information. The cloud-base height is estimated based on the vertical
distributions of density scale height, temperature lapse rate and relative humidity using GPS
wet retrievals. The proposed algorithm is tested upon 31 cloudy GPS RO profiles from
Constellation Observing System for Meteorology, Ionosphere and Climate (COSMIC). It is
found that the retrieval temperature is warmer than NCEP-reanalysis in the upper levels of
the cloud and colder near and below the cloud base. Dropsonde observations for Hurricane
Rita confirm this characteristic feature of the NCEP temperature analysis within clouds. The
cloud thickness and cloud-base height that are determined by the proposed criteria are
validated qualitatively with IR and VIS satellite images. Sensitivity of the GPS in-cloud
profile retrieval to the MODIS cloud top pressure is also shown.
The preliminary steps of assimilating AIRS radiance data into a mesoscale model are presented. First, a stand-alone 1D-Var
driver is developed in order to retrieve temperature and specific humidity profiles from AIRS data using background
profiles obtained from a mesoscale model. Vertical background error covariance matrices are calculated for both
temperature and specific humidity. The inverses of the background error covariance matrices are estimated using a
singular value decomposition procedure, in which the small singular values and associated small-scale structures in the
background error covariances are removed. By comparing with two available collocated radiosonde data, it is then
shown that AIRS radiance-derived vertical profiles of temperature and specific humidity are more consistent to
radiosonde observations than the background profiles. Finally, a multi-profile retrieval is performed which produced
largest analysis increments of temperature and moisture in the region of a mid- and upper-level moisture gradient
associated with a cold front.
Important issues involving the assimilation of rain-affected observations using an adjoint mesoscale modeling
system are addressed in this study. The adjoint model of the explicit moist physics parameterization is included
in the modeling system, which allows for the calculation of gradients with respect to the initial hydrometeor
concentrations (cloud water/ice, rain, snow, and graupel). Cloud-scale idealized four dimensional variational data
assimilation experiments demonstrate the benefit of assimilating precipitation information and the ability of the
adjoint model to produce useful gradients with respect to the hydrometeor fields. The agreement between model
fields and observations is greater (especially for the early forecast hydrometeor fields) when rainy observations
are incorporated into the assimilation process versus only assimilating conventional model data (windspeeds,
temperature, pressure). Additional data assimilation experiments are conducted with microwave radiances.
These data improve the initial precipitation structure of a tropical cyclone. These experiments are promising
steps for the incorporation of rain-affected observations in operational data assimilation systems.