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NASA NTRSDate not supplied

Assessment of Retrieved GMI Emissivity Over Land, Snow and Sea Ice in the GEOS System

Measurements from microwave sounders and imagers provide a valuable source of information including atmospheric temperature and water vapor in Numerical Weather Prediction (NWP) systems that assimilate these observations directly over water surfaces (oceans and other large water bodies). In a recent decadal survey, targeted observables in the Planetary Boundary Layer (PBL) were cited as a key need for future observations (NASEM, 2018). Microwave observations which sense in the PBL are currently available, however, utilizing surface-sensitive microwave observations for atmospheric data assimilation remains a challenge over land, snow and sea ice. This is in part due to the inability of surface emissivity models used by NWP data assimilation systems to simulate observations with sufficient accuracy. The GEOS-ADAS (Todling and el Akkraoui, 2018) which utilizes the Community Radiative Transfer Model (CRTM) (Han, 2006; Chen 2009) is no exception. The ECMWF system has retrieved instantaneous surface emissivity from surface-sensitive channels for SSMI/S and MHS radiance observations, and apply these estimates to the closest channels higher in frequency (Baordo and Geer 2016) in the calculation of simulated radiances. This approach currently is also being tested in the GEOS-ADAS for AMSU-A and ATMS radiances (Zhu et al. 2021). No or minimal emissivity spectral variability has been assumed in the above-mentioned studies. Recently, work by Munchak et al., 2020 (hereby referred to as M2020) provided a new database for emissivity over land, snow and sea ice retrieved from the NASA Global Precipitation Mission (GPM). Compared with Tool to Estimate Land Surface Emissivities at Microwave (TELSEM2; Wang et al., 2017), M2020 provides emissivities for more frequencies(i.e., 10.7 GHz V/H). Moreover, this database is unique in that it utilizes both active and passive data to retrieve surface emissivity and normalized radar cross section. While the emissivity values may be useful for other sensors, they are most applicable to the GPM Microwave Imager (GMI). In this work the GEOS-ADAS is modified to utilize emissivity values from Munchak et al, 2020 in place of values used by CRTM. Presently, only GMI radiances over ocean are used in the operational GEOS-ADAS. This study will focus on the GMI radiances over land, snow, and ice, as a first attempt to evaluate GMI radiances over these non-water surface types. Two cases are then presented, one with one week of observation minus background departures using the modified GEOS-ADAS, and one utilizing the original GEOS-ADAS. It should be noted that the surface emissivity models in CRTM are not state of the art and are scheduled to be replaced by the Community Surface Emissivity Module (CSEM; Chen and Weng, 2016). Simulations using default CRTM emissivity values are used merely as reference comparing against M2020, and is not a thorough comparison against other more state of the art modules such as CSEM.

GMI↗
NASA NTRSDate not supplied

Recent Developments in the Assimilation of Microwave and Radar Observations Into NWP Models

Microwave observations play a very important role in improving the weather forecasts. Although these observations are routinely assimilated into NWP models in clear-sky conditions, assimilation of all-sky microwave observations is very limited. Two main factors contributing to this limitation are inaccuracy in the input cloud and hydrometeor profiles used as input to the radiative transfer model and also error in scattering calculations performed by the radiative transfer model itself. The Mie theory is used by many fast RT models to estimate the optical properties of single particles. The Mie theory assumes spherical shapes for ice or snow particles with mixture of air and ice. However, hydrometeors scattering radiation at microwave frequencies have different shapes, sizes, and orientations. Therefore, using Mie theory to determine their optical properties leads to large uncertainties in all-sky radiative transfer calculations. The discrete dipole approximation (DDA) which approximates the optical properties of large objects in terms of discrete dipoles has shown promise in calculating the scattering properties of particles with different shapes in the microwave frequencies. This presentation focuses on recent advancements in the CRTM scattering calculations for frozen hydrometeors in the microwave frequencies using the DDA technique. In addition to using stand-alone CRTM calculations using collocated ATMS and reanalysis profiles, the data assimilation experiments conducted using the NOAA FV3GFS forecast system are used to evaluate the scattering improvements. Additionally, the backscattering information from the DDA database was used to implement a radar simulator into CRTM. The radar operator takes advantage of CRTM different modules to calculate clouds absorption and scattering properties. In addition to the forward model both adjoint and tangent linear of the radar simulator are implemented and evaluated as well. The radar simulator is currently being tested within the JEDI/GEOS data assimilation framework to facilitate the assimilation of radar measurements such as CloudSat CPR and GPM DPR into the NASA GEOS model.

Isaac Moradi↗
NASA NTRS2022

Assessment of Retrieved GMI Emissivity Over Land, Snow, and Sea Ice in the GEOS System

Directly assimilating microwave radiances over land, snow and sea ice remains a significant challenge for data assimilation systems. These data assimilation systems are critical to the success of global numerical weather prediction systems including the Global Earth Observing System-Atmospheric Data Assimilation System (GEOS-ADAS). Extending more surface sensitive microwave channels over land, snow and ice could provide a needed source of data for Numerical Weather Prediction particularly in the Planetary Boundary Layer (PBL). Unfortunately, the accuracy of emissivity models currently available within the GEOS-ADAS along with other data assimilation systems are insufficient to simulate and assimilate radiances. Recently, Munchak et al. (2020) published a 5-year climatological database for retrieved microwave emissivity from the GPM Microwave Imager (GMI) aboard the Global Precipitation Mission (GPM). In this work the database is utilized by modifying the GEOS-ADAS to use this emissivity database in place of the default emissivity value available in the Community Radiative Transfer Model (CRTM), which is the fast radiative transfer model used by the GEOS-ADAS. As a first step, the GEOS-ADAS is run in a so-called “stand-alone” mode to simulate radiances from GMI using the default CRTM emissivity, and replacing the default CRTM emissivity models with values from Munchak et al, 2020. The simulated observations using Munchak et al., 2020 agree more closely with observations from GMI. These results are presented along with a discussion of the implication for GMI observations within the GEOS-ADAS.

GPM↗
NASA NTRS2018

Preliminary Evaluation of Influence of Aerosols on the Simulation of Brightness Temperature in the NASA's Goddard Earth Observing System Atmospheric Data Assimilation System

This document reports on preliminary results obtained when studying the impact of aerosols on the calculation of brightness temperature (BT) for satellite infrared (IR) instruments that are currently assimilated in a 3DVAR configuration of Goddard Earth Observing System (GEOS)-atmospheric data assimilation system (ADAS). A set of fifteen aerosol species simulated by the Goddard Chemistry Aerosol Radiation and Transport (GOCART) model is used to evaluate the influence of the aerosol fields on the Community Radiative Transfer Model (CRTM) calculations taking place in the observation operators of the Gridpoint Statistical Interpolation (GSI) analysis system of GEOSADAS. Results indicate that taking aerosols into account in the BT calculation improves the fit to observations over regions with significant amounts of dust. The cooling effect obtained with the aerosol-affected BT leads to a slight warming of the analyzed surface temperature (by about 0:5oK) in the tropical Atlantic ocean (off northwest Africa), whereas the effect on the air temperature aloft is negligible. In addition, this study identifies a few technical issues to be addressed in future work if aerosol-affected BT are to be implemented in reanalysis and operational settings. The computational cost of applying CRTM aerosol absorption and scattering options is too high to justify their use, given the size of the benefits obtained. Furthermore, the differentiation between clouds and aerosols in GSI cloud detection procedures needs satisfactory revision.

3DVAR↗
NASA NTRS2014

Limb Correction of RGB Composite Imagery for Improved Interpretation

RGB (Red‐Green‐Blue) imagery, created by integrating several spectral channels into one composite image, is currently used by the operational weather community to aid in quick, real‐time analysis of atmospheric processes. However, the limb effect - a result of an increasing optical path length of the absorbing atmosphere between the satellite and the earth as scan angle increases - interferes with the qualitative interpretation of RGB composites at large scan angles. It also makes the comparison of similar products from multiple satellite sensors difficult. Recent work has indicated that correcting for the limb effect in the basic channel imagery using simple statistical relationships greatly improves the utility of the derived RGB imagery. However, it is hypothesized that the limb correction coefficients vary with respect to latitude, season, cloud cover, and surface albedo. This poster will highlight an improved approach to the limb correction of RGB imagery using varying coefficients. The Joint Center for Satellite and Data Assimilation (JCSDA) Community Radiative Transfer Model (CRTM) was used to simulate top of atmosphere brightness temperatures at varying scan angles for infrared channels corresponding to the Aqua and Terra Moderate Resolution Imaging Spectroradiometer (MODIS), Suomi NPP Visible Infrared Imaging Radiometer Suite (VIIRS), and Meteosat‐10 Spinning Enhanced Visible and Infrared Imager (SEVIRI) sensors. A subset of European Center for Medium‐Range Weather Forecasts (ECMWF) temperature, specific humidity, and ozone mixing ratio profiles from March 2013 through February 2014 were used as input to the CRTM. The simulated brightness temperatures were used to determine the best fit slope of the linear relationship between the natural log of the cosine of the scan angle and the difference of the simulated brightness temperature at nadir and on the limb. The correction coefficients were then analyzed for variability with respect to latitude, season, cloud cover, and surface type and used to produce improved limb corrected imagery. Applications of the results will be presented.

Elmer, Nicholas↗
NASA NTRS2014

Land Surface Microwave Emissivity Dynamics: Observations, Analysis and Modeling

Land surface microwave emissivity affects remote sensing of both the atmosphere and the land surface. The dynamical behavior of microwave emissivity over a very diverse sample of land surface types is studied. With seven years of satellite measurements from AMSR-E, we identified various dynamical regimes of the land surface emission. In addition, we used two radiative transfer models (RTMs), the Community Radiative Transfer Model (CRTM) and the Community Microwave Emission Modeling Platform (CMEM), to simulate land surface emissivity dynamics. With both CRTM and CMEM coupled to NASA's Land Information System, global-scale land surface microwave emissivities were simulated for five years, and evaluated against AMSR-E observations. It is found that both models have successes and failures over various types of land surfaces. Among them, the desert shows the most consistent underestimates (by approx. 70-80%), due to limitations of the physical models used, and requires a revision in both systems. Other snow-free surface types exhibit various degrees of success and it is expected that parameter tuning can improve their performances.

Modeling↗
NASA NTRSDate not supplied

Assimilation of Active MW and Radar Observations in the NWP Models

Passive instruments lack the ability to provide vertically resolved measurements of clouds and precipitation, which can be obtained by active radar instruments. However, incorporating these active measurements into data assimilation systems presents challenges due to the absence of fast forward radiative transfer models and difficulties in error modeling. This talk focuses on the development, evaluation, and sensitivity analysis of a forward radar model integrated into CRTM. The forward radar model utilizes scattering properties obtained from hydrometeor lookup tables generated using the discrete dipole approximation. By utilizing CRTM instrument-specific coefficients, the model can calculate both reflectivity and attenuated reflectivity for any given radar instrument and zenith angles. Evaluation using CloudSat measurements demonstrates a strong agreement between simulations and observations when the input profiles of hydrometeors align with the measured reflectivity profiles.

Isaac Moradi↗
NASA NTRS2010

Satellite Data Simulator Unit: A Multisensor, Multispectral Satellite Simulator Package

Several multisensor simulator packages are being developed by different research groups across the world. Such simulator packages [e.g., COSP , CRTM, ECSIM, RTTO, ISSARS (under development), and SDSU (this article), among others] share overall aims, although some are targeted more on particular satellite programs or specific applications (for research purposes or for operational use) than others. The SDSU or Satellite Data Simulator Unit is a general-purpose simulator composed of Fortran 90 codes and applicable to spaceborne microwave radiometer, radar, and visible/infrared imagers including, but not limited to, the sensors listed in a table. That shows satellite programs particularly suitable for multisensor data analysis: some are single satellite missions carrying two or more instruments, while others are constellations of satellites flying in formation. The TRMM and A-Train are ongoing satellite missions carrying diverse sensors that observe clouds and precipitation, and will be continued or augmented within the decade to come by future multisensor missions such as the GPM and Earth-CARE. The ultimate goals of these present and proposed satellite programs are not restricted to clouds and precipitation but are to better understand their interactions with atmospheric dynamics/chemistry and feedback to climate. The SDSU's applicability is not technically limited to hydrometeor measurements either, but may be extended to air temperature and humidity observations by tuning the SDSU to sounding channels. As such, the SDSU and other multisensor simulators would potentially contribute to a broad area of climate and atmospheric sciences. The SDSU is not optimized to any particular orbital geometry of satellites. The SDSU is applicable not only to low-Earth orbiting platforms as listed in Table 1, but also to geostationary meteorological satellites. Although no geosynchronous satellite carries microwave instruments at present or in the near future, the SDSU would be useful for future geostationary satellites with a microwave radiometer and/or a radar aboard, which could become more feasible as engineering challenges are met. In this short article, the SDSU algorithm architecture and potential applications are reviewed in brief.

Masunaga, Hirohiko↗
NASA NTRS2011

Global Precipitation Measurement, Validation, and Applications Integrated Hydrologic Validation to Improve Physical Precipitation Retrievals for GPM

Land surface modeling and data assimilation can provide dynamic land surface state variables necessary to support physical precipitation retrieval algorithms over land. It is well-known that surface emission, particularly over the range of frequencies to be included in the Global Precipitation Measurement Mission (GPM), is sensitive to land surface states, including soil properties, vegetation type and greenness, soil moisture, surface temperature, and snow cover, density, and grain size. In order to investigate the robustness of both the land surface model states and the microwave emissivity and forward radiative transfer models, we have undertaken a multi-site investigation as part of the NASA Precipitation Measurement Missions (PMM) Land Surface Characterization Working Group. Specifically, we will demonstrate the performance of the Land Information System (LIS; http://lis.gsfc.nasa.gov; Peters-Lidard et aI., 2007; Kumar et al., 2006) coupled to the Joint Center for Satellite Data Assimilation (JCSDA's) Community Radiative Transfer Model (CRTM; Weng, 2007; van Deist, 2009). The land surface is characterized by complex physical/chemical constituents and creates temporally and spatially heterogeneous surface properties in response to microwave radiation scattering. The uncertainties in surface microwave emission (both surface radiative temperature and emissivity) and very low polarization ratio are linked to difficulties in rainfall detection using low-frequency passive microwave sensors (e.g.,Kummerow et al. 2001). Therefore, addressing these issues is of utmost importance for the GPM mission. There are many approaches to parameterizing land surface emission and radiative transfer, some of which have been customized for snow (e.g., the Helsinki University of Technology or HUT radiative transfer model;) and soil moisture (e.g., the Land Surface Microwave Emission Model or LSMEM).

Peters-Lidar, Christa D.↗
NASA NTRS2012

Application of Artificial Neural Networks to the Development of Improved Multi-Sensor Retrievals of Near-Surface Air Temperature and Humidity Over Ocean

Improved estimates of near-surface air temperature and air humidity are critical to the development of more accurate turbulent surface heat fluxes over the ocean. Recent progress in retrieving these parameters has been made through the application of artificial neural networks (ANN) and the use of multi-sensor passive microwave observations. Details are provided on the development of an improved retrieval algorithm that applies the nonlinear statistical ANN methodology to a set of observations from the Advanced Microwave Scanning Radiometer (AMSR-E) and the Advanced Microwave Sounding Unit (AMSU-A) that are currently available from the NASA AQUA satellite platform. Statistical inversion techniques require an adequate training dataset to properly capture embedded physical relationships. The development of multiple training datasets containing only in-situ observations, only synthetic observations produced using the Community Radiative Transfer Model (CRTM), or a mixture of each is discussed. An intercomparison of results using each training dataset is provided to highlight the relative advantages and disadvantages of each methodology. Particular emphasis will be placed on the development of retrievals in cloudy versus clear-sky conditions. Near-surface air temperature and humidity retrievals using the multi-sensor ANN algorithms are compared to previous linear and non-linear retrieval schemes.

Roberts, J. Brent↗
NASA NTRS2018

Using Multi-Sensor Aerosol Optical Depth Retrievals to Improve Infrared Radiance Assimilation

Coarse dust aerosols absorb terrestrial radiation leading to significant longwave heating/cooling rates. Modules accounting for aerosol impacts on radiation have been implemented into CRTM framework, but operational centers continue to assume aerosol-free conditions when assimilating infrared radiances into NWP models. This assumption can introduce significant biases in analysis fields (temp, moisture, etc.), which can reduce forecast skill.

Naeger, Aaron↗
NASA NTRS2019

Progress Toward Implementation of Coupled Ocean/Atmosphere in Unified Forward Operator (UFO)

The (Sea-Ice Ocean and Coupled Assimilation) SOCA project focuses on meshing atmospheric and marine data assimilation efforts to create innovative data assimilation systems. One of the main goals ofSOCA is to make use of surface-sensitive radiances to constrain sea-ice and upper ocean fields (e.g., salinity, temperature, sea-ice fraction, sea-ice temperature, etc.). the focus of this research is to build first elements toward an ocean/sea-ice/atmosphere coupled data assimilation capability, with a focus on supporting and developing the assimilation of observations sensitive to multiple sub-domains. We Setup the existing Joint Effort for Data assimilation Integration (JEDI) coupled UFO (CRTM related) for surface sensitive microwave radiances (SST and SSS) focusing on Global Precipitation Measurement (GPM) Imager (GMI) and Soil Moisture Active Passive (SMAP).

Ebrahimi, Hamideh↗
NASA NTRSDate not supplied

Improving the Use of Surface-Sensitive Radiances in the GMAO Hybrid-4DEnVar System

The planetary boundary layer (PBL) was designated as an incubation-class targeted observable (TO) in the 2018 Decadal Survey. As no single instrument source will provide enough information to constrain the global PBL, the assimilation of a wide range of observations in data assimilation (DA) systems will play a critical role, and GMAO has put in efforts to enhance surface-sensitive radiance assimilation. Although a vast number of microwave radiance observations are used in the GEOS global DA system, very few surface-sensitive radiances are currently used over land due to large uncertainty of land surface emissivity in the CRTM as well as cloud detection issue. Dynamically varying emissivity is retrieved from observations of window channels in the GEOS system for non-scattering FOVs and applied to sounding channel assimilation. Moreover, the original radiance bias correction is found not to work well over land, and shows drifting bias when the original emissivity sensitivity bias predictor with a dynamically varying emissivity retrieval is used. Hence, the radiance bias correction has been modified, and the quality control procedure has been adapted accordingly. Cycled experiment results show improvement in the temperature forecast at lower model levels; further refinement of this system continues.

Yanqiu Zhu↗
NASA NTRSDate not supplied

Assimilation of CrIS Shortwave Infrared Channels into the GEOS Atmospheric Data Assimilation System

In recent years, there has been a renewed interest in using the 4.3 μmshortwave infrared (SWIR) band for temperature sounding. This is in part brought on by proposed cubesat missions sensing the 4.3 μmband such as MiSTIC and CIRAS. Jones et al. has shown that shortwave infrared channels on CrIScan be used effectively in NOAA's Global Forecast and Data Assimilation System (GDAS). In this work a similar study is presented using the Goddard Earth Observing System - Atmospheric Data Assimilation System (GEOS-ADAS). Results from Observing System Experiments (OSEs) utilizing SWIR CrISare presented using standard community accepted forecast metrics including Forecast Sensitivity to Observation Impact (FSOI), and an assessment of vertical sensitivity using Jacobians from the Community Radiative Transfer Model (CRTM). The implications and utility within the GEOS-ADAS for future NASA GMAO products are discussed.

Bryan M Karpowicz↗
NASA NTRSDate not supplied

Toward Enhancing the Use of IASI and CrIS Surface-Sensitive Radiances Over Land in the NASA GMAO GEOS Data Assimilation Framework

Assimilating surface-sensitive radiances over land is still challenging for both infrared (IR) and microwave (WV) radiances essentially because of the large uncertainties of the land physical surface emissivity model used in the Community Radiative Transfer Model (CRTM) and the uncertainties of land surface state properties. Currently very few IR radiances are assimilated over land. Large number of IR radiances are rejected by the surface sensitivity checks as well as the cloud detection check. In this study, we identified the appropriate Infrared Atmospheric Sounding Interferometer (IASI) and Cross-track Infrared Sounder (CrIS) surface-sensitive channels to retrieve Land Surface Temperature (LST). Then, we studied the impacts of these retrieved LST and retuned cloud detection on the simulation and assimilation of IASI and CrIS in the NASA GEOS in clear sky conditions. The preliminary results are shown to enhance the rate of IASI and CrIS assimilated channels over land. The impacts on the quality of the resulting analysis and subsequent forecast will be presented at the meeting.

Niama Boukachaba↗
NASA NTRSDate not supplied

Toward Improving the Assimilation of IASI and CrIS Radiances Over Land Into the NASA GEOS: LST Inversion and Validation

Assimilating surface-sensitive radiances over land is still challenging for both infrared (IR) and microwave (WV) essentially because of the large uncertainties of the land physical surface emissivity model used in the CRTM and the uncertainties of land surface state properties. Currently very few IR radiances are assimilated over land in the NASA Goddard Earth Observing System (GEOS). Large number of radiances are rejected by the emissivity sensitivity check as well as the Cloud detection check. This study focuses on enhancing the assimilation of Infrared Atmospheric Sounding Interferometer (IASI) and Cross-track Infrared Sounder (CrIS) over land in the GEOS forecasting and data assimilation framework. To reach this goal, the Land surface Temperature (LST) is first inverted using IR radiances from IASI and CrIS selected channels to use it as surface boundary parameter for the assimilation of the rest of IASI and CrIS surface-sensitive channels. This work will present a full assessment of the quality of this LST by comparing it and its spatio-temporal variability to LST predicted by the GEOS model. The impacts on the quality of the resulting analysis and subsequent forecast will also be discussed.

Niama Boukachaba↗
NASA NTRSDate not supplied

Status and Progress of All-Sky Hyperspectral IR Radiance Assimilation in GEOS

The majority of hyperspectral infrared radiance observations that peak in the lower and mid-troposphere have been excluded from the assimilation in the operational GEOS by the cloud detection scheme. The significance and challenges of assimilating these cloud-affected observations have attracted the attention of the research community, encouraged by the progress in utilizing all-sky microwave radiances affected by clouds and precipitation in many data assimilation systems, including GEOS. While the Global Modeling and Assimilation Office (GMAO) has made significant progress on the assimilation of cloud-cleared infrared radiances, this parallel study marks the first effort to directly assimilate cloud-affected hyperspectral infrared radiances in the GEOS. This initial study is focused on CrIS-FSR wa:wqter-vapor channels. The capability of the GEOS model and CRTM in simulating infrared radiances affected by clouds has been evaluated, and the all-sky brightness temperature Jacobians with respect to temperature, specific humidity, and hydrometeors are examined closely in varying cloud conditions. With hydrometeor control variables in the GEOS, several important aspects of all-sky IR radiance assimilation are investigated. The symmetric cloud effects are assessed for their robustness when used as the cloud proxy in observation error modeling that incorporates the inter-channel correlations with error situation dependency on the amount of cloud. The effective radius, bias correction and quality control processes will also be adapted to accommodate modifications necessary for all-sky infrared assimilation before comprehensive four-dimensional ensemble-variational(4dEnVar) experiments are conducted to assess their impacts on the analysis and forecast performance of GEOS.

Wei Gu↗
NASA NTRSDate not supplied

Geophysical Retrievals and Cloud Analyses from Merged Airborne Radiometer Datasets Covering 10–684 GHz

Airborne microwave radiometers provide insight about numerous aspects of Earth’s atmosphere and yield critical validation datasets for spaceborne radiometers. Three radiometers that are important to NASA’s airborne remote-sensing arsenal include: the Advanced Microwave Precipitation Radiometer (AMPR), covering 10–85 GHz; the Conical Scanning Millimeter-wave Imaging Radiometer (CoSMIR), covering 50–183 GHz; and the Compact Scanning Submillimeter-wave Imaging Radiometer (CoSSIR), covering 170–684 GHz. The NASA field campaigns of interest to this study include: the Integrated Precipitation and Hydrology Experiment (IPHEx) in 2014, the Olympic Mountains Experiment and Radar Definition Experiment (OLYMPEX/RADEX) in 2015–2016, the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) in 2020–2023, and the Airborne Lightning Observatory for FEGS and TGFs (ALOFT) in 2023. To provide a more comprehensive perspective on clouds and precipitation observed during these airborne field campaigns, AMPR data were merged spatiotemporally with CoSMIR data for IPHEx, OLYMPEX/RADEX, and IMPACTS (2020 and 2022), while considering differences in instrument characteristics and operations, providing brightness temperature (Tb) values from 10–183 GHz in a common background grid throughout each flight. AMPR and CoSSIR data were similarly merged for IMPACTS (2023) and ALOFT, providing a common background grid with Tb values covering 10–684 GHz throughout each flight. These merged Tb data were employed in geophysical retrievals using the Community Radiative Transfer Model (CRTM), an Eddington radiative transfer model, and a one-dimensional variational (1DVAR) inversion method. Retrievals of cloud liquid water path were of primary interest. This presentation will include an overview of the methods for the radiometer data mergers, the radiative transfer methods, the geophysical retrievals, and detailed results from examining trends in Tb and cloud liquid water path in clouds, precipitation, and cloud-to-precipitation transition zones.

Corey G Amiot↗