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NASA NTRS2019

Early Results for Version 06 IMERG

The U.S. Global Precipitation Measurement mission (GPM) science team is developing a long-term dataset based on intercalibrated estimates from the international constellation of precipitation-relevant satellites and other data. The Integrated Multi-satellitE Retrievals for GPM (IMERG) merged precipitation product (IMERG) is computed at the half hour, 0.1° x 0.1° resolution globally in three "Runs" Early, Late, and Final (4 hours, 14 hours, and 3.5 months after observation time, respectively). The longer latencies increase the available input data for the resulting estimates, most notably the use of monthly precipitation gauge analyses in the Final run. The Early and Late runs use a climatological gauge adjustment as a proxy for the monthly gauge analyses. At meeting time GPM should be well into computing the new Version 06, which will be the first time IMERG covers the last two decades and routinely provides morphed estimates in polar regions where the surface is snow- and ice-free. In this talk a few salient features of the IMERG algorithm will be summarized, then representative examples of IMERG products will be shown. This starts with basic results, such as animations of near-real-time maps, then extends to preliminary analyses of dataset characteristics. For example, the accumulations during Hurricane Harvey around Houston, Texas, USA, tended to be low, while accumulations along the Texas/Louisiana border to the northeast tended to be high. Furthermore, these opposite-sign differences occurred more or less simultaneously over much of the accumulation period. The working hypothesis is that there were systematic differences in the convective "regime" in the two places. The talk will end with a quick summary of the processing status and the future course of IMERG development.

Huffman, George J.↗
NASA NTRS2011

Collaboration Portals for NASA's Airborne Field Campaigns

The University of Alabama in Huntsville (UAH), in collaboration with the Global Hydrology Resource Center, a NASA Earth Science Data Center, has provided information management for a number of NASA Airborne Field campaigns, both hurricane science investigations and satellite instrument validation. Effective field campaign management requires communication and coordination tools, including utilities for personnel to upload and share flight plans, weather forecasts, a variety of mission reports, preliminary science data, and personal photos. Beginning with the Genesis and Rapid Intensification Processes (GRIP) hurricane field campaign in 2010, we have provided these capabilities via a Drupal-based collaboration portal. This portal was reused and modified for the Midlatitude Continental Convective Clouds Experiment (MC3E), part of the Global Precipitation Measurement mission ground validation program. An end goal of these development efforts is the creation of a Drupal profile for field campaign management. This presentation will discuss experiences with Drupal in developing and using these collaboration portals. Topics will include Drupal modules used, advantages and disadvantages of working with Drupal in this context, and how the science teams used the portals in comparison with other communication and collaboration tools.

Conover, Helen↗
NASA NTRS2011

GPM Mission Overview

The Global Precipitation Measurement (GPM) Mission is an international satellite mission to unify and advance precipitation measurements from a constellation of research and operational sensors to provide "next-generation" precipitation products. Relative to current global rainfall products, GPM data products will be characterized by: (1) more accurate instantaneous precipitation measurements (especially for light rain and cold-season solid/snow precipitation), (2) more frequent sampling by an expanded constellation of microwave radiometers that include operational humidity sounders over land, (3) inter-calibrated microwave brightness temperatures from constellation radiometers within a unified framework, and (4) physical-based precipitation retrievals from constellation radiometers using a common a priori cloud hydrometeor database derived from GPM Core sensor measurements. The cornerstone of the GPM mission is the deployment of a Core Observatory in a unique 65 degree non-Sun-synchronous orbit to serve as a physics observatory and a calibration reference to improve precipitation measurements by a constellation of dedicated and operational passive microwave sensors. The Core Observatory will carry a KulKa-band Dual-frequency Precipitation Radar (DPR) and a multi-channel (10-183 GHz) GPM Microwave Radiometer (GMI). The combined use ofDPR and GMI measurements will place greater constraints on possible solutions to radiometer retrievals to improve the accuracy and consistency of precipitation retrievals from all constellation radiometers. As a science mission with integrated application goals, GPM is designed to (1) advance precipitation measurement capability from space through combined use of active and passive microwave sensors, (2) advance the knowledge of the global water/energy cycle and freshwater availability through better description of the space-time variability of global precipitation, and (3) improve weather, climate, and hydrological prediction capabilities through more accurate and frequent measurements of instantaneous precipitation rates and time-integrated rainfall accumulation. An overview of the GPM mission concept and science activities in the United States, together with an update on international collaborations in radiometer intercalibration and ground validation, will be presented.

Hou, Arthur Y.↗
NASA NTRS2021

Applications of Dynamic Land Surface Information for Passive Microwave Precipitation Retrieval

Accurate, physically based precipitation retrieval over global land surfaces is an important goal of the NASA/JAXA Global Precipitation Measurement Mission (GPM). This is a difficult problem for the passive microwave constellation, as the signal over radiometrically warm land surfaces in the microwave frequencies means that the measurements used are indirect and typically require inferring some type of relationship between an observed scattering signal and precipitation at the surface. GPM, with collocated radiometer and dual-frequency radar, is an excellent tool for tackling this problem and improving global retrievals. In the years following the launch of the GPM Core Observatory satellite, physically based passive microwave retrieval of precipitation over land continues to be challenging. Validation efforts suggest that the operational GPM passive microwave algorithm, the Goddard profiling algorithm (GPROF), tends to overestimate precipitation at the low (<5 mm h−1) end of the distribution over land. In this work, retrieval sensitivities to dynamic surface conditions are explored through enhancement of the algorithm with dynamic, retrieved information from a GPM-derived optimal estimation scheme. The retrieved parameters describing surface and background characteristics replace current static or ancillary GPROF information including emissivity, water vapor, and snow cover. Results show that adding this information decreases probability of false detection by 50% and, most importantly, the enhancements with retrieved parameters move the retrieval away from dependence on ancillary datasets and lead to improved physical consistency.

Sarah Ringerud↗
NASA NTRSDate not supplied

Version 8 of the SMAP Level-4 Soil Moisture Data Assimilation Product

The NASA Soil Moisture Active Passive (SMAP) mission Level-4 Soil Moisture (L4_SM) product provides global, 9-km resolution, 3-hourly surface (0-5 cm) and root-zone (0-100 cm) soil moisture from April 2015 to present with a mean latency of 2.5 days from the time of observation. The product is based on the assimilation of SMAP L-band (1.4 GHz) brightness temperature (Tb) observations into the NASA Catchment land surface model as the model is driven with observations-based precipitation forcing. This presentation discusses the improvements in the forthcoming Version 8 of L4_SM, including updates in the precipitation forcing, the Catchment model parameters, and the L-band microwave radiative transfer model (mwRTM). The precipitation observations used in L4_SM Version 8 outside of North America and the high latitudes are from the latest (Version 7) NASA Integrated Multi-satellitE Retrievals for the Global Precipitation Measurement mission (IMERG) products. Moreover, occasionally excessive precipitation rates in earlier versions of L4_SM along certain longitudes in North America were eliminated by a bug fix in the precipitation corrections algorithm. The Catchment model in L4_SM Version 8 uses climatological snow albedo values based on observations from the Moderate Resolution Imaging Spectroradiometer, replacing the look-up table parameterization of earlier versions. Additionally, corrected soil parameters were implemented for a small region in Argentina that had erroneously been classified as peat because of an error in the ancillary soil data. Finally, the mwRTM in L4_SM Version 8 uses the Mironov soil mixing approach and updated values of the L-band scattering albedo, soil roughness, and vegetation opacity climatology obtained from the latest (Release 19) SMAP Level-2 dual-channel soil moisture retrieval product. During the development of L4_SM Version 8, the change in the mwRTM parameterization resulted in a reduced unbiased RMSE of surface soil moisture when verified against in situ measurements. It also reduced the standard deviation of the Tb observation-minus-forecast residuals by ~0.15 K, highlighting the importance of the mwRTM for successful data assimilation. The bug fixes in the precipitation corrections algorithm and the Catchment model soil parameters in the Argentina region result in locally large improvements of the simulated land surface states. In summary, the ongoing refinements of the L4_SM product continue to improve its science quality and performance for global soil moisture monitoring

Rolf Reichle↗
NASA NTRSDate not supplied

Detection of Drizzle and Light Snowfall Over the Southern Ocean Using the GPM Combined Algorithm

Following the 2014 launch of the Global Precipitation Measurement Mission (GPM), an unprecedented combination of coincident active and passive microwave observations are available for state of the art precipitation retrieval. The GPM Combined Algorithm forms the backbone of this effort, optimizing geophysical variables for agreement with the multi-spectral information content. Combined retrievals are then utilized, along with a radiative transfer model, as a database applied for retrievals across a constellation of passive microwave radiometers of varying frequencies and viewing geometries. By keeping such retrievals related through the transfer standard of the combined algorithm, level 3 products such as the Integrated Multi-satellitE Retrievals for GPM (IMERG) are able to provide consistent global products for users at the higher temporal resolution required for hydrological applications on the global scale. In the current version of the combined product, precipitation retrievals are carried out only in the presence of a signal from the active radar. As a result, light precipitation and drizzle below the threshold of radar sensitivity are not included in any of the products down the chain from the constellation to IMERG. In this work, the effects of enhancing the retrievals with an optimal estimation-type (OE) water vapor and cloud retrieval using the passive observations are explored over the Southern Ocean on a regional scale. Non-convergence of the OE in areas with no detectable radar signal is used to identify areas with high probability of light precipitation and drizzle. Microphysical scale characterization of the light precipitation will be explored using information derived from the higher sensitivity CloudSat mission along with model information to identify and associate the related atmospheric state and dynamics. This is a physically-based approach requiring radiometric consistency with all available multi-spectral observations. Retrieved drizzle can then be included in the constellation databases continuing across all scales through to the level 3 products. The technique, successfully demonstrated for the Southern Ocean, can be easily adapted for light precipitation retrieval over other areas and surfaces leading to a global climatology of light precipitation.

Sarah Rinegerud↗
NASA NTRS2019

Enhancing GPM Passive and Combined Microwave Algorithms with Dynamic Surface Information for Drizzle Retrieval and Improved Precipitation Detection Over Land

Following the 2014 launch of the Global Precipitation Measurement Mission (GPM), an unprecedented combination of coincident active and passive microwave observations are available for state of the art precipitation retrieval. The GPM Combined Algorithm forms the backbone of this effort, optimizing geophysical variables for agreement with the full suite of multi-spectral information content. These combined retrievals are then utilized, along with a radiative transfer model, as a database applied for retrievals across a constellation of passive microwave radiometers of varying frequencies. By keeping such retrievals related through the transfer standard of the combined algorithm, level 3 products such as the Integrated Multi-satellitE Retrievals for GPM (IMERG) are able to provide consistent global products for users at the higher temporal resolution required for hydrological applications. In initial versions of the combined product, precipitation retrievals are carried out only in the presence of a signal from the active radar. As a result, light precipitation and drizzle below the threshold of DPR sensitivity are not included in any of the products down the chain from the constellation to IMERG. In this work, the effects of enhancing the retrievals with a surface emissivity and non-raining water vapor retrieval using the passive observations are explored. Over both ocean and land, the surface retrieval is used to identify areas with high probability of light precipitation and drizzle which is then quantified using techniques derived from the higher sensitivity CloudSat mission. Results indicate successful inclusion of drizzle in the retrievals that can then be included in the constellation databases, as well as improvement in passive microwave false positive precipitation signals over land in cases where surface scattering was misinterpreted as precipitation signal. The inclusion of the dynamic surface information also creates a more robust, radiometrically consistent retrieval scheme for process studies and hydrologic applications.

Ringerud, Sarah↗
NASA NTRSDate not supplied

Do Better Satellite Precipitation Algorithms Improve Landslide Hazard Assessment?

Satellites make it possible to estimate precipitation in near real time. Given the challenges of achieving global coverage by other means, these data are used widely. However, few systems for landslide hazard assessment rely on satellite precipitation estimates. This could be due in part to perceptions of accuracy, although latency, spatial resolution, and other factors may also be important. We test whether recent changes to data streams from the Global Precipitation Measurement mission (GPM) have improved its potential for use in landslide prediction. Specifically, we examine data produced by the Integrated Multi-satellitERetrievals for the GPM (IMERG) algorithm, which was upgraded to version 7 this year. IMERG relies upon other algorithms, including the Goddard Profiling Algorithm (GPROF) and the GPM Combined Radar-Radiometer Algorithm (CORRA). Many changes have been made during the switch from IMERG version 6 to version 7. These include upgrading CORRA and GPROF to version 7, to improve the accuracy of precipitation in frozen, mountainous, and coastal areas. The measured intensity of some storms has been enhanced with a new algorithm, the Scheme for Histogram Adjustment with Ranked Precipitation Estimates in the Neighborhood. Combined with many others, these changes to IMERG should improve its utility for landslide hazard assessment in a variety of contexts. To test this idea, we retrain the global Landslide Hazard Assessment for Situational Awareness (LHASA) model twice—first with data from IMERG version 6B and second with 7B. Since current daily rainfall is the most important variable in determining outcomes predicted by LHASA, it should reflect changes made to that input. First, we grid the landslides at a daily, thirty-arcsecond resolution. This serves as the response variable. At each of these sites current and antecedent rainfall are extracted, along with antecedent snow mass and soil moisture, slope, and PGA. In addition, one million grid cells are selected at random points to represent conditions under which landslides (probably) do not occur. After merging these data, we hold back 20% of the dataset for validation purposes and train a machine-learning model with the rest. We assess both the model’s overall ability to identify landslides and its ability to predict specific large landslide disasters.

Thomas A Stanley↗
NASA NTRS2010

Florida Agriculture - Utilizing TRMM to Analyze Sea Breeze Thunderstorm Patterns During El Nino Southern Oscillations and Their Effects Upon Available Fresh Water for South Florida Agricultural Planning and Management

This project utilizes Tropical Rainfall Measuring Mission (TRMM) and Landsat satellite data to assess the impact of sea breeze precipitation upon areas of agricultural land use in southern Florida. Water is a critical resource to agriculture, and the availability of water for agricultural use in Florida continues to remain a key issue. Recent projections of statewide water use by 2020 estimate that 9.3 billion gallons of water per day will be demanded, and agriculture represents 47% of this demand (Bronson 2003). Farmers have fewer options for water supplies than public users and are often limited to using available supplies from surface and ground water sources which depend in part upon variable weather patterns. Sea breeze thunderstorms are responsible for much of the rainfall delivered to Florida during the wet season (May-October) and have been recognized as an important overall contributor of rainfall in southern Florida (Almeida 2003). TRMM satellite data was used to analyze how sea breeze-induced thunderstorms during El Nino and La Nina affected interannual patterns of precipitation in southern Florida from 1998-2009. TRMM's Precipitation Radar and Microwave Imager provide data to quantify water vapor in the atmosphere, precipitation rates and intensity, and the distribution of precipitation. Rainfall accumulation data derived from TRMM and other microwave sensors were used to analyze the temporal and spatial variations of rainfall during each phase of the El Nino Southern Oscillation (ENSO). Through the use of TRMM and Landsat, slight variations were observed, but it was determined that neither sea breeze nor total rainfall patterns in South Florida were strongly affected by ENSO during the study period. However, more research is needed to characterize the influence of ENSO on summer weather patterns in South Florida. This research will provide the basis for continued observations and study with the Global Precipitation Measurement Mission.

Billiot, Amanda↗
NASA NTRS2002

Estimation of Mesoscale Atmospheric Latent Heating Profiles from TRMM Rain Statistics Utilizing a Simple One-Dimensional Model

In this study, a model is developed to estimate mesoscale-resolution atmospheric latent heating (ALH) profiles. It utilizes rain statistics deduced from Tropical Rainfall Measuring Mission (TRMM) data, and cloud vertical velocity profiles and regional surface thermodynamic climatologies derived from other available data sources. From several rain events observed over tropical ocean and land, ALH profiles retrieved by this model in convective rain regions reveal strong warming throughout most of the troposphere, while in stratiform rain regions they usually show slight cooling below the freezing level and significant warming above. The mesoscale-average, or total, ALH profiles reveal a dominant stratiform character, because stratiform rain areas are usually much larger than convective rain areas. Sensitivity tests of the model show that total ALH at a given tropospheric level varies by less than +/- 10 % when convective and stratiform rain rates and mesoscale fractional rain areas are perturbed individually by +/- 15 %. This is also found when the non-uniform convective vertical velocity profiles are replaced by one that is uniform. Larger variability of the total ALH profiles arises when climatological ocean- and land-surface temperatures (water vapor mixing ratios) are independently perturbed by +/- 1.0 K (+/- 5%) and +/- 5.0 K (+/- 15%), respectively. At a given tropospheric level, such perturbations can cause a +/- 25% variation of total ALH over ocean, and a factor-of-two sensitivity over land. This sensitivity is reduced substantially if perturbations of surface thermodynamic variables do not change surface relative humidity, or are not extended throughout the entire model evaporation layer. The ALH profiles retrieved in this study agree qualitatively with tropical total diabatic heating profiles deduced in earlier studies. Also, from January and July 1999 ALH-profile climatologies generated separately with TRMM Microwave Imager and Precipitation Radar rain statistics, it is shown that ALH profiles can be retrieved utilizing diverse satellite-derived rain products that offer convective and stratiform discrimination. Therefore, the ALH retrieval model developed in this study can be used to make regional estimates of total diabatic heating profiles in the future Global Precipitation Measurement mission, and to assimilate these profiles into numerical weather forecast and climate models.

Iacovazzi, Robert A., Jr.↗
NASA NTRS2001

Estimation of Mesoscale Atmospheric Latent Heating Profiles from TRMM Rain Statistics Utilizing a Simple One-Dimensional Model

In this study, a model is developed to estimate mesoscale-resolution atmospheric latent heating (ALH) profiles. It utilizes rain statistics deduced from Tropical Rainfall Measuring Mission (TRMM) data, and cloud vertical velocity profiles and regional surface thermodynamic climatologies derived from other available data sources. From several rain events observed over tropical ocean and land, ALH profiles retrieved by this model in convective rain regions reveal strong warming throughout most of the troposphere, while in stratiform rain regions they usually show slight cooling below the freezing level and significant warming above. The mesoscale-average, or total, ALH profiles reveal a dominant stratiform character, because stratiform rain areas are usually much larger than convective rain areas. Sensitivity tests of the model show that total ALH at a given tropospheric level varies by less than +/- 10 % when convective and stratiform rain rates and mesoscale fractional rain areas are perturbed individually by 1 15 %. This is also found when the non-uniform convective vertical velocity profiles are replaced by one that is uniform. Larger variability of the total ALH profiles arises when climatological ocean- and land-surface temperatures (water vapor mixing ratios) are independently perturbed by +/- 1.0 K (+/- 5 %) and +/- 5.0 K (+/- 15 %), respectively. At a given tropospheric level, such perturbations can cause a +/- 25 % variation of total ALH over ocean, and a factor-of-two sensitivity over land. This sensitivity is reduced substantially if perturbations of surface thermodynamic variables do not change surface relative humidity, or are not extended throughout the entire model evaporation layer. The ALH profiles retrieved in this study agree qualitatively with tropical total diabatic heating profiles deduced in earlier studies. Also, from January and July 1999 ALH-profile climatologies generated separately with TRMM Microwave Imager and Precipitation Radar rain statistics, it is shown that ALH profiles can be retrieved utilizing diverse satellite-derived rain products that offer convective and stratiform discrimination. Therefore, the ALH retrieval model developed in this study can be used to make regional estimates of total diabatic heating profiles in the future Global Precipitation Measurement mission, and to assimilate these profiles into numerical weather forecast and climate models.

Iacovazzi, Robert A., Jr.↗
NASA NTRS2023

Regional Analysis of the 2015–16 Lower Mekong River Basin Drought Using NASA Satellite Observations

Study region Lower Mekong River Basin (LMRB) Study focus Satellite remote sensing products are widely used for monitoring droughts. Using NASA satellite sensors of precipitation (Global Measurement Mission, GPM), soil moisture (Soil Moisture Active and Passive, SMAP), and terrestrial water storage (Gravity Recovery and Climate Experiment, GRACE), this study evaluates the historical drought in the LMRB during 2015–16. SMAP soil moisture was validated against in-situ soil moisture, and GPM precipitation and SMAP soil moisture were cross-validated with streamflow observations. The spatiotemporal dynamics of soil moisture were also examined in different ranges of catchment areas. In performing the analysis, we used lagged correlations between hydrological variables and the indices of the Standardized Precipitation Index (SPI) and Standardized Streamflow Index (SSI). New hydrological insights for the regions Spatio-temporal patterns of drought in 2015–16 were examined from the entire basin to small watersheds. A mismatch occurs when using GRACE data to study droughts in small watersheds (many of the small watersheds would be a fraction of the few 100 km2 spatial resolutions of GRACE pixel). In smaller watersheds, hydrological drought (SSI) was closely defined with SMAP soil moisture downscaled to 1 km rather than the meteorological drought index (SPI). By leveraging satellite-based observations across a range of spatial scales, this study highlights the utility of Earth observations in informing water resources and land management decisions at the regional scale.

Mekong↗
NASA NTRS2007

Overview of TRMM Data Products and Services

November 27, 2007 marks the l0th anniversary of the launch of the Tropical Rainfall Measuring Mission (TRMM) satellite. In anticipation of this anniversary, this paper will present an overview of the various TRMM data products currently available including the standard products, near real-time products, special products, and prototype products. It also will present an easy way to obtain these data. TRMM standard products have been publicly available since a few months after launch in November 1997. TRMM is currently on version 6 of the data product. Version 3 was the "at launch" version. The approval for each of these versions came through the Joint TRMM Science Team. Standard products are divided into 3 categories: single TRMM instrument, Visible Infrared Scanner (VIRS), TRMM Microwave Imager (TMI), and Precipitation Radar (PR); combined TRMM products (PR and TMI); finally TRMM and other satellites (combined, TMI, SSMI, AMSRE, AMSU). The single TRMM instrument products are processed through 4 levels: Level lA, science data packets processed into orbital files; Level 1B and lC, geolocated data at the instrument field of view; Level 2, geolocated, geophysical parameters at the instrument field of view; Level 3, time aggregated, gridded geophysical parameters. These products are available with 24 hours of production through an anonymous ftp account on trmmopen.gsfc.nasa.gov. The TRMM data system started to produce near real-time products at the end of 1999. They are currently available only through a controlled user account. However, approval to get access to this account can be obtained by sending a note to Erich.F.Stoclter@nasa.gov providing the reason for access and contact information including a valid email. TRMM is not restricting access but needs the information to determine the usefulness of near-real time data to the general science community including applications agencies. TRMM near real-time products are swath products up to Level 2 of processing. The oldest data in the swath is generally no older than 120 minutes when it becomes available to the community. The real-time products including a VIRS level lB, a TMI parameter reduced 1B, a TMI level 2 parameter reduced rain product, a PR level 2 surface rain product, and a PR level 2 rain product with 25 vertical levels. Currently, TRMM also produces a gridded 3 hour global merged product from several radiometers including AMSU and from radiometercalibrated IR data. The paper also describes several simple-format gridded text products available fiom the trmmopen.gsfc.nasa.gov anonymous fip server denoted as 3668 products. These products were produced to provide rain estimates from the three TRMM instruments in a universal format (ASCII) that requires very little data format knowledge. The paper goes on to describe prototype L1 radiometer products that apply an early intercalibration approach that provides a starting point to be used for Global Precipitation Measurement mission radiometer products. The paper also provides a brief overview of a precipitation features data product being produced using TRMM products including the Lighting Imaging Sensor (LIS) using an algorithm developed at the University of Utah and distributed by that organization. The paper concludes with some possible changes to products that are planned for the next reprocessing cycle and special services such as geographical subsetting available to the science community.

Stocker, Erich Franz↗
NASA NTRS2012

Impact of Non-Uniform Beam Filling on Spaceborne Cloud and Precipitation Radar Retrieval Algorithms

In this presentation we will discuss the performance of classification and retrieval algorithms for spaceborne cloud and precipitation radars such as the Global Precipitation Measurement mission Dual-frequency Precipitation Radar (GPM/DPR), and notional radar for the Aerosol/Clouds/Ecosystem (ACE) mission and related concepts. Spaceborne radar measurements are simulated either from Airborne Precipitation Radar 2nd Generation observations, or from atmospheric model outputs via instrument simulators contained in the NASA Earth Observing Systems Simulators Suite (NEOS(sup 3)). Both methods account for the three dimensional nature of the scattering field at resolutions smaller than that of the spaceborne radar under consideration. We will focus on the impact of non-homogeneities of the field of hydrometeors within the beam. We will discuss also the performance of methods to identify and mitigate such conditions, and the resulting improvements in retrieval accuracy. The classification and retrieval algorithms analyzed in this study are those derived from APR-2's Suite of Processing and Retrieval Algorithms (ASPRA); here generalized to operate on an arbitrary set of radar configuration parameters to study the expected performance of spaceborne cloud and precipitation radars. The presentation will highlight which findings extend to other algorithm families and which ones do not.

Aerosol/Clouds/Ecosystem (ACE)↗
NASA NTRS2022

Direct Comparisons Between GPM-DPR and CloudSat Snowfall Retrievals

Two spaceborne radars currently in orbit enable the sampling of snowfall near the surface and throughout the atmospheric column, namely, CloudSat’s Cloud Profiling Radar (CPR) and the Global Precipitation Measurement mission’s Dual-Frequency Precipitation Radar (GPM-DPR). In this paper, a direct comparison of the CPR’s 2C-SNOW-PROFILE (2CSP), the operational GPM-DPR algorithm (2ADPR) and a neural network (NN) retrieval applied to the GPM-DPR data is performed using coincident observations between both radars. Examination of over 3500 profiles within moderate to strong precipitation (Ka band ≥ 18 dBZ) show that the NN retrieval provides the closest retrieval of liquid equivalent precipitation rate R immediately above the melting level to the R retrieved just below the melting layer, agreeing within 5%. Meanwhile, 2CSP retrieves a maximum value of R at −15°C, decreases by 35% just above the melting layer, and is about 50% smaller than the GPM-DPR retrieved R below the melting layer. CPR-measured reflectivity shows median reduction of 2–3 dB from −15° to −2.5°C, likely the reason for the 2CSP retrieval reduction of R . Two case studies from NASA field campaigns [i.e., Olympic Mountains Experiment (OLYMPEX) and Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS)] provide analogs to the type of precipitating systems found in the comparison between retrieval products. For the snowfall events that GPM-DPR can observe, this work suggests that the 2CSP retrieval is likely underestimating the unattenuated reflectivity, resulting in a potential negative, or low, bias in R . Future work should investigate how frequently the underestimated reflectivity profiles occur within the CPR record and quantify its potential effects on global snowfall accumulation estimation.

Atmosphere↗
NASA NTRS2024

Enhancing Consistency of Microphysical Properties of Precipitation Across the Melting Layer in the Dual-Frequency Precipitation Radar Data

Stratiform rain and the overlying ice play crucial roles in Earth's climate system. From a microphysics standpoint, water mass flux primarily depends on two variables: particles' concentration and their mass. The Dual-frequency Precipitation Radar (DPR) on the Global Precipitation Measurement mission core satellite is a spaceborne instrument capable of estimating these two quantities through dual-wavelength measurements. In this study, we evaluate bulk statistics on the ice particle properties derived from dual-wavelength radar data in relation to the properties of rain underneath. Specifically, we focus on DPR observations over stratiform precipitation, characterized by columns exhibiting a prominent bright band, where the melting layer can be easily detected. Our analysis reveals a large increase in the retrieved mass flux as we transition from the ice to the rain phase in the official DPR product. This observation is in disagreement with our expectation that mass flux should remain relatively stable across the bright band in cold-rain conditions. To address these discrepancies, we propose an alternative retrieval algorithm that ensures a gradual transition of Dm (mean mass-weighted particle melted-equivalent diameter) and the precipitation rate across the melting zone. This approach also helps in estimating bulk ice density above the melting level. These findings demonstrate that DPR observations can not only quantify ice particle content and their size above stratiform rain regions but also estimate bulk density, provided uniform conditions that minimize uncertainties related to partial beam filling.

radar measurements from space↗
DOE OSTI2022

QRF4P-NRT: Probabilistic Post-Processing of Near-Real-Time Satellite Precipitation Estimates Using Quantile Regression Forests

Accurate and reliable near-real-time satellite precipitation estimation is of great importance for operational large-scale flood forecasting and drought monitoring. The state-of-the-art precipitation post-processing model is based on a deterministic approach to construct relationships between satellites estimates and ground observations. We propose a probabilistic postprocessor, the Probabilistic Post-Processing of Near-Real-Time Satellite Precipitation Estimates using Quantile Regression Forests (QRF4P-NRT), based on quantile modeling, yielding both deterministic and probabilistic predictions. The experimental design incorporates different solutions of near-real-time predictors to further improve the model performance. Using the Integrated Multi-satellitE Retrievals Early Run for Global Precipitation Measurement Mission (IMERG-E) product as an example, we illustrate that the proposed method significantly improves the overall quality of the raw IMERG-E and is also superior to the bias-corrected product (IMERG Final Run, IMERG-F) at daily scale in a complex mountain basin. Evaluations of the corrected IMERG-E, raw IMERG-E, and IMERG-F using ground observation show that the corrected IMERG-E improves correlation coefficients (0.7), mean error (-0.14 mm/day) and root mean square error (3.3 mm/day) relative to the raw IMERG-E (0.31, -0.72 and 5.5 mm/day) and IMERG-F (0.34, -0.09 and 6.0 mm/day). The error decomposition further confirms that the QRF4P-NRT improves on the various deficiencies of the raw IMERG-E product. The ensemble assessment also demonstrates that the quantile outputs provide reliable prediction spread and sharp prediction intervals. The promising results indicate the great potential of the proposed method for probabilistic post-processing for near-real-time satellite precipitation estimates, and for further applications such as hydrological ensemble forecasting.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Trends in Warm Season Mesoscale Convective Systems Over Asia in 2001–2020

Mesoscale convective systems (MCSs) frequently occur over Asia during the warm season, often producing intense precipitation with associated socioeconomic impacts. Here we reveal significant trends in MCS occurrence frequency and related precipitation in Asia during the warm season (March–September) in 2001–2020, using a tracking method that combines cloud and precipitation criteria with high-resolution satellite data from the Global Precipitation Measurement mission. To examine whether there are differences between MCSs of different scales, both meso-α scales (MαCSs) and meso-β scales (MβCSs), with horizontal scales of 200–2,000 km and 20–200 km, are tracked. The distribution pattern of frequency and related precipitation of both MαCSs and MβCSs are quite similar and manifest positive trends over East Asia (EA) and Northeast Asia, and negative trend over Southeast Asia (SEA). The MCS precipitation trend contributes significantly to total precipitation trend, with MαCSs contributing the most. Our analysis indicates the trend in lower-tropospheric water vapor flux convergence has a similar spatial pattern to the MCS frequency and related precipitation trend. Based on an atmospheric moisture flux decomposition analysis, the water vapor flux convergence trend can largely be explained by the change in horizontal wind convergence, while the specific humidity trend driven largely by temperature change plays a minor role. In conclusion, the trend in wind convergence in EA and SEA is possibly related to the evident trend in the lower-tropospheric anticyclone over the western North Pacific and SEA, which might be due to the relatively stronger warming in the Indian Ocean during the past two decades.

54 ENVIRONMENTAL SCIENCES↗