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NASA NTRS2026

PERSIANN-Unet: A Global Deep Learning Framework for Near-Real-Time Precipitation Estimation Using Infrared Data

Access to high-quality, high-resolution, near-real-time precipitation data is essential for hydrological and meteorological research and disaster mitigation. Traditional tools such as rain gauges and radar networks, though effective, have limitations, including sparse coverage in remote areas and high operational costs. Satellite data, with its global coverage and high spatial and temporal resolutions, mitigates limitations in coverage. Satellite precipitation products like Hydro Estimator (HE), Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG), and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) utilize both geosynchronous thermal infrared (IR) and passive microwave (PMW) data in their operation. PMW sensors offer detailed atmospheric profiles but suffer from higher latency, whereas IR sensors provide lower latency but only capture cloud-top information. Despite this constraint, IR data remains attractive for low-latency precipitation estimation. Recent advances in deep learning, particularly convolutional neural networks (CNNs), have further improved satellite precipitation retrievals. This study introduces PERSIANN-Unet (PUnet or PERSIANN V3), a quasi-global algorithm covering 60°N–60°S that combines IR data, monthly climatology, and the UNet architecture to produce half-hourly precipitation estimates at 0.04° resolution. The product is evaluated against HE, IMERG, and PDIR-Now for 2022–2023. Results show that PUnet closely matches its training target, IMERG V07 Final, at the global scale, and performance is further evaluated against Stage IV as a reference over CONUS. Training PUnet on IMERG (2016–2021) leverages a high-quality, integrated PMW IR-gauge precipitation product while developing an IR-based framework not reliant on PMW availability. By operating on a single global image, PUnet avoids tile partitioning and blending steps, reducing edge discontinuities, and produces more spatially consistent precipitation fields across hemispheres.

Phu Nguyen↗
NASA NTRS2022

Development and Evaluation of Ensemble Consensus Precipitation Estimates over High Mountain Asia

Precipitation estimates are highly uncertain in complex regions such as High-Mountain Asia (HMA), where ground measurements are very difficult to obtain, and atmospheric dynamics poorly understood. Though gridded products derived from satellite-based observations and/or reanalysis can provide temporally and spatially distributed estimates of precipitation, there are significant inconsistencies in these products. As such, to date, there is little agreement in the community on the best and most accurate gridded precipitation product in HMA, which is likely area dependent because of HMA’s strong heterogeneities and complex orography. Targeting these gaps, this article presents the development of a consensus ensemble precipitation product using three gridded precipitation datasets (the Integrated Multi-satellitE Retrieals for Global Precipitation Measurement IMERG, the Climate Hazards group Infrared Precipitation with Stations CHIRPS, and the ECMWF Reanalysis ERA5) with a localized probability matched mean (LPM) approach. We evaluate the performance of the LPM estimate along with a simple ensemble mean (EM) estimate to overcome the differences and disparities of the three selected constituent products on long-term averages and trends in HMA. Our analysis demonstrates that LPM reduces the high biases embedded in the ensemble members and provides more realistic spatial patterns compared to EM. LPM is also a good alternative for merging data products with different spatio-temporal resolutions. By filtering disparities among the individual ensemble members, LPM overcomes the problem of a certain product performing well only in a particular area and provides a consensus estimate with plausible temporal trends.

Fadji Z Maina↗
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 NTRS2011

The NASA CloudSat/GPM Light Precipitation Validation Experiment (LPVEx)

Ground-based measurements of cool-season precipitation at mid and high latitudes (e.g., above 45 deg N/S) suggest that a significant fraction of the total precipitation volume falls in the form of light rain, i.e., at rates less than or equal to a few mm/h. These cool-season light rainfall events often originate in situations of a low-altitude (e.g., lower than 2 km) melting level and pose a significant challenge to the fidelity of all satellite-based precipitation measurements, especially those relying on the use of multifrequency passive microwave (PMW) radiometers. As a result, significant disagreements exist between satellite estimates of rainfall accumulation poleward of 45 deg. Ongoing efforts to develop, improve, and ultimately evaluate physically-based algorithms designed to detect and accurately quantify high latitude rainfall, however, suffer from a general lack of detailed, observationally-based ground validation datasets. These datasets serve as a physically consistent framework from which to test and refine algorithm assumptions, and as a means to build the library of algorithm retrieval databases in higher latitude cold-season light precipitation regimes. These databases are especially relevant to NASA's CloudSat and Global Precipitation Measurement (GPM) ground validation programs that are collecting high-latitude precipitation measurements in meteorological systems associated with frequent coolseason light precipitation events. In an effort to improve the inventory of cool-season high-latitude light precipitation databases and advance the physical process assumptions made in satellite-based precipitation retrieval algorithm development, the CloudSat and GPM mission ground validation programs collaborated with the Finnish Meteorological Institute (FMI), the University of Helsinki (UH), and Environment Canada (EC) to conduct the Light Precipitation Validation Experiment (LPVEx). The LPVEx field campaign was designed to make detailed measurements of cool-season light precipitation by leveraging existing infrastructure in the Helsinki Precipitation Testbed. LPVEx was conducted during the months of September--October, 2010 and featured coordinated ground and airborne remote sensing components designed to observe and quantify the precipitation physics associated with light rain in low-altitude melting layer environments over the Gulf of Finland and neighboring land mass surrounding Helsinki, Finland.

Petersen, Walter A.↗
NASA NTRS2019

Status and Examples for the Version 06 IMERG Multi-Satellite Products

After five years of development following the launch of the Global Precipitation Measurement (GPM) missionCore Observatory, the GPM data products are now being extended across the joint Tropical Rainfall MeasuringMission (TRMM) and GPM eras. Version 06 of the U.S. GPM team's Integrated Multi-satellitE Retrievals forGPM (IMERG) merged precipitation product provides a consistent intercalibration for all precipitation productscomputed from individual satellites with the TRMM and GPM Core Observatory sensors as the TRMM- andGPM-era calibrators, respectively, and incorporates monthly surface gauge data. One major change in the basicIMERG algorithm for V06 is that precipitation motion vectors (used to drive the quasi-Lagrangian interpolation,or "morphing") are computed by tracking vertically integrated vapor (TQV) fields analyzed in MERRA2 andGEOS5. This innovation provides globally complete coverage, expanding IMERG's coverage beyond the 60N-Slatitude band previously provided by IR-based vectors, although precipitation over snowy/icy surfaces is stillmasked out as unreliable. A second innovation is that the Quality Index (QI) data field computed for the half-hourlydatasets has been refined to include estimates of correlation at microwave overpass times.We will summarize the processing status for V06 IMERG, for which the retrospective processing shouldbe actively advancing at meeting time. We will show early examples of performance. For example, the TQVmotion vectors are typically slightly better than the IR-based vectors at all latitudes. The transition across theTRMM/GPM data boundary will be discussed, including the necessity of filling in the TRMM-based calibrationsover the latitude band 35-65 in each hemisphere. The notional schedule for the eventual retirement of thepredecessor TRMM Multi-satellite Precipitation Analysis (TMPA) multi-satellite dataset will be updated as well.

Huffman, George J.↗
NASA NTRS2021

Assimilation of DAWN Doppler Wind Lidar Data During the 2017 Convective Processes Experiment (CPEX): Impact on Precipitation and Flow Structure

An improved representation of 3-D air motion and precipitation structure through forecast models and assimilation of observations is vital for improvements in weather forecasting capabilities. However, there are few independent data to properly validate a model forecast of precipitation structure when the underlying dynamics are evolving on short convective timescales. Using data from the JPL Ku/Ka-band Airborne Precipitation Radar (APR-2) and the 2 μmDoppler Aerosol Wind (DAWN) lidar collected during the2017 Convective Processes Experiment (CPEX), the NASA Unified Weather Research and Forecasting (WRF) Ensemble Data Assimilation System (EDAS) modeling system was used to quantify the impact of high-resolution sparsely sampled DAWN measurements on the analyzed variables and on the forecast when the DAWN winds were assimilated. Over-all, the assimilation of the DAWN wind profiles had a discernible impact on the wind field as well as the evolution and timing of the 3-D precipitation structure. Analysis of individual variables revealed that the assimilation of the DAWN winds resulted in important and coherent modifications of the environment. It led to an increase in the near-surface convergence, temperature, and water vapor, creating more favorable conditions for the development of convection exactly where it was observed (but not present in the control run). Comparison to APR-2 and observations by the Global Precipitation Measurement (GPM) satellite shows a much-improved forecast after the assimilation of the DAWN winds – development of precipitation where there was none, more organized precipitation where there was some, and a much more intense and organized cold pool, similar to the analysis of the dropsonde data. The onset of the vertical evolution of the precipitation showed similar radar-derived cloud-top heights, but delayed in time. While this investigation was limited to a single CPEX flight date, the investigation design is appropriate for further investigation of the impact of airborne Doppler wind lidar observations upon short-term convective precipitation forecasts

DAWN↗
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 NTRS2017

Weather from 250 Miles Up: Visualizing Precipitation Satellite Data (and Other Weather Applications) Using CesiumJS

Geospatial weather visualization remains predominately a two-dimensional endeavor. Even popular advanced tools like the Nullschool Earth display 2-dimensional fields on a 3-dimensional globe. Yet much of the observational data and model output contains detailed three-dimensional fields. In 2014, NASA and JAXA (Japanese Space Agency) launched the Global Precipitation Measurement (GPM) satellite. Its two instruments, the Dual-frequency Precipitation Radar (DPR) and GPM Microwave Imager (GMI) observe much of the Earth's atmosphere between 65 degrees North Latitude and 65 degrees South Latitude. As part of the analysis and visualization tools developed by the Precipitation Processing System (PPS) Group at NASA Goddard, a series of CesiumJS [Using Cesium Markup Language (CZML), JavaScript (JS) and JavaScript Object Notation (JSON)] -based globe viewers have been developed to improve data acquisition decision making and to enhance scientific investigation of the satellite data. Other demos have also been built to illustrate the capabilities of CesiumJS in presenting atmospheric data, including model forecasts of hurricanes, observed surface radar data, and gridded analyses of global precipitation. This talk will present these websites and the various workflows used to convert binary satellite and model data into a form easily integrated with CesiumJS.

web applications↗
NASA NTRS2011

Sensitivity of Spaceborne and Ground Radar Comparison Results to Data Analysis Methods and Constraints

With the availability of active weather radar observations from space from the Precipitation Radar (PR) on board the Tropical Rainfall Measuring Mission (TR.MM) satellite, numerous studies have been performed comparing PR reflectivity and derived rain rates to similar observations from ground-based weather radars (GR). These studies have used a variety of algorithms to compute matching PR and GR volumes for comparison. Most studies have used a fixed 3-dimensional Cartesian grid centered on the ground radar, onto which the PR and GR data are interpolated using a proprietary approach and/or commonly available GR analysis software (e.g., SPRINT, REORDER). Other studies have focused on the intersection of the PR and GR viewing geometries either explicitly or using a hybrid of the fixed grid and PR/GR common fields of view. For the Dual-Frequency Precipitation Radar (DPR) of the upcoming Global Precipitation Measurement (GPM) mission, a prototype DPR/GR comparison algorithm based on similar TRMM PR data has been developed that defines the common volumes in terms of the geometric intersection of PR and GR rays, where smoothing of the PR and GR data are minimized and no interpolation is performed. The PR and GR volume-averaged reflectivity values of each sample volume are accompanied by descriptive metadata, for attributes including the variability and maximum of the reflectivity within the sample volume, and the fraction of range gates in the sample average having reflectivity values above an adjustable detection threshold (typically taken to be 18 dBZ for the PR). Sample volumes are further characterized by rain type (Stratiform or Convective), proximity to the melting layer, underlying surface (land/water/mixed), and the time difference between the PR and GR observations. The mean reflectivity differences between the PR and GR can differ between data sets produced by the different analysis methods; and for the GPM prototype, by the type of constraints and categorization applied to the data. In this paper, we will show results comparing the 3-D gridded analysis "black box" approach to the GPM prototype volume-matching approach, using matching TRMM PR and WSR-88D ground radar data. The affects of applying data constraints and data categorizations on the volume-matched data to the results will be shown, and explanations of the differences in terms of data and analysis algorithm characteristics will be presented. Implications of the differences to the determination of PR/DPR calibration differences and use of ground radar data to evaluate the PR and DPR attenuation correction algorithms will be discussed.

Morris, Kenneth R.↗
NASA NTRSDate not supplied

NASA GPM Status and Future Activities

The joint U.S.-Japan Global Precipitation Measurement (GPM) mission is approaching a decade of operations, and continues to pursue research, dataset production, and outreach related to precipitation. One key activity over the last year was the release of an improved “Version 07” of all GPM precipitation and latent heating products. This talk summarizes key improvements to the GPM products for which NASA has lead responsibility and provides some examples of the changes between Versions 06 and 07 in algorithm performance. One important operational change that affected Version 07 is that the scanning strategy for the Ka-band radar channel changed in May 2018; all products that depend on Ka were revised to accommodate this change. For example, in Version 07 the Goddard Profiling (GPROF) algorithm has implemented improvements in regions where orographic enhancement and suppression take place and where the surface is snowy/icy, and again covers radiometers reaching back to 1987. The Combined Radar Radiometer Algorithm (CORRA) now incorporates modified drop-size distribution constraints that substantially reduce bias. Revisions to the Convective-Stratiform Heating (CSH) algorithm employ new radiative transfer retrievals as well as accounting for terrain in the vertical coordinates. Each algorithm was adjusted to ensure continuity for each product across the boundary in 2014 between the predecessor Tropical Rainfall Measuring Mission (TRMM) and the GPM Core Observatory. The U.S. Science Team’s Integrated Multi-satellitE Retrievals for GPM (IMERG) was upgraded to account for distortions in the probability density function of regional precipitation rates due to weighted averaging in the Kalman filter used for “morphing” the passive microwave data. The talk will conclude by considering major issues that require continued attention, including the use of machine learning algorithms, the operational challenge of swarms of “small”, perhaps short-lived satellites, and estimates of the remaining lifespan of the Core Observatory.

Global Precipitation Measuremen↗
DOE OSTI2021

Precipitation Characteristics and Future Changes Over the Southern Slope of Tibetan Plateau Simulated by a High-Resolution Global Nonhydrostatic Model

Current climate model resolution cannot accurately describe the complex topography over the Tibetan Plateau, which limits our understanding of past and future precipitation over this region. This study investigates the daily precipitation characteristics and its future changes over the Tibetan Plateau, especially for the southern slope of Tibetan Plateau (SSTP), by 14-km Nonhydrostatic ICosahedral Atmospheric Model (NICAM) with explicitly calculated convection for historical and future 30-years period. By comparing with the satellite Global Precipitation Measurement (GPM), NICAM well reproduces the historical precipitation spatial pattern, seasonal cycle, and the extreme precipitation belt over SSTP, but overestimates precipitation amount by~35%. It is found that heavy precipitation probability decreases as elevation becomes higher while the light precipitation generally shows the opposite. For the precipitation changes during June to September from 1979–2008 to 2075–2104, NICAM predicts that mean precipitation will decrease over low-level SSTP but increase over high-level SSTP. Nonprecipitation and heavy precipitation probability will increase while light precipitation probability will decrease in the future over SSTP. The extreme precipitation probability and intensity will increase ~50%/°C and ~8%/°C over SSTP, and this increase is more obvious as elevation becomes higher. The robust increase of extreme precipitation along the SSTP topography is unique and has not been identified by the climate model simulations before. The strong meridional gradient of specific humidity over SSTP is found to be further enhanced under global warming, and this gradient enhancement is suggested to be responsible for the increase in the extreme precipitation over SSTP.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS2011

Sensitivity of C-Band Polarimetric Radar-Based Drop Size Distribution Measurements to Maximum Diameter Assumptions

The estimation of rain drop size distribution (DSD) parameters from polarimetric radar observations is accomplished by first establishing a relationship between differential reflectivity (Z(sub dr)) and the central tendency of the rain DSD such as the median volume diameter (D0). Since Z(sub dr) does not provide a direct measurement of DSD central tendency, the relationship is typically derived empirically from rain drop and radar scattering models (e.g., D0 = F[Z (sub dr)] ). Past studies have explored the general sensitivity of these models to temperature, radar wavelength, the drop shape vs. size relation, and DSD variability. Much progress has been made in recent years in measuring the drop shape and DSD variability using surface-based disdrometers, such as the 2D Video disdrometer (2DVD), and documenting their impact on polarimetric radar techniques. In addition to measuring drop shape, another advantage of the 2DVD over earlier impact type disdrometers is its ability to resolve drop diameters in excess of 5 mm. Despite this improvement, the sampling limitations of a disdrometer, including the 2DVD, make it very difficult to adequately measure the maximum drop diameter (D(sub max)) present in a typical radar resolution volume. As a result, D(sub max) must still be assumed in the drop and radar models from which D0 = F[Z(sub dr)] is derived. Since scattering resonance at C-band wavelengths begins to occur in drop diameters larger than about 5 mm, modeled C-band radar parameters, particularly Z(sub dr), can be sensitive to D(sub max) assumptions. In past C-band radar studies, a variety of D(sub max) assumptions have been made, including the actual disdrometer estimate of D(sub max) during a typical sampling period (e.g., 1-3 minutes), D(sub max) = C (where C is constant at values from 5 to 8 mm), and D(sub max) = M*D0 (where the constant multiple, M, is fixed at values ranging from 2.5 to 3.5). The overall objective of this NASA Global Precipitation Measurement Mission (GPM/PMM Science Team)-funded study is to document the sensitivity of DSD measurements, including estimates of D0, from C-band Z(sub dr) and reflectivity to this range of D(sub max) assumptions. For this study, GPM Ground Validation 2DVD's were operated under the scanning domain of the UAHuntsville ARMOR C-band dual-polarimetric radar. Approximately 7500 minutes of DSD data were collected and processed to create gamma size distribution parameters using a truncated method of moments approach. After creating the gamma parameter datasets the DSD's were then used as input to a T-matrix model for computation of polarimetric radar moments at C-band. All necessary model parameterizations, such as temperature, drop shape, and drop fall mode, were fixed at typically accepted values while the D(sub max) assumption was allowed to vary in sensitivity tests. By hypothesizing a DSD model with D(sub max) (fit) from which the empirical fit to D0 = F[Z(sub dr)] was derived via non-linear least squares regression and a separate reference DSD model with D(sub max) (truth), bias and standard error in D0 retrievals were estimated in the presence of Z(sub dr) measurement error and hypothesized mismatch in D(sub max) assumptions. Although the normalized standard error for D0 = F[Z(sub dr)r] can increase slightly (as much as from 11% to 16% for all 7500 DSDs) when the D(sub max) (fit) does not match D(sub max) (truth), the primary impact of uncertainty in D(sub max) is a potential increase in normalized bias error in D0 (from 0% to as much as 10% over all 7500 DSDs, depending on the extent of the mismatch between D(sub max) (fit) and D(sub max) (truth)). For DSDs characterized by large Z(sub dr) (Z(sub dr) > 1.5 to 2.0 dB), the normalized bias error for D0 estimation at C-band is sometimes unacceptably large (> 10%), again depending on the extent of the hypothesized D(sub max) mismatch. Modeled errors in D0 retrievals from Z(sub dr) at C-band are demonstrated in detail and comparedo similar modeled retrieval errors at S-band and X-band where the sensitivity to D(sub max) is expected to be less. The impact of D(sub max) assumptions to the retrieval of other DSD parameters such as Nw, the liquid water content normalized intercept parameter, are also explored. Likely implications for DSD retrievals using C-band polarimetric radar for GPM are assessed by considering current community knowledge regarding D(sub max) and quantifying the statistical distribution of Z(sub dr) from ARMOR over a large variety of meteorological conditions. Based on these results and the prevalence of C-band polarimetric radars worldwide, a call for more emphasis on constraining our observational estimate of D(sub max) within a typical radar resolution volume is made

Carey, Lawrence D.↗
NASA NTRS2013

Detection Thresholds of Falling Snow From Satellite-Borne Active and Passive Sensors

There is an increased interest in detecting and estimating the amount of falling snow reaching the Earths surface in order to fully capture the global atmospheric water cycle. An initial step toward global spaceborne falling snow algorithms for current and future missions includes determining the thresholds of detection for various active and passive sensor channel configurations and falling snow events over land surfaces and lakes. In this paper, cloud resolving model simulations of lake effect and synoptic snow events were used to determine the minimum amount of snow (threshold) that could be detected by the following instruments: the W-band radar of CloudSat, Global Precipitation Measurement (GPM) Dual-Frequency Precipitation Radar (DPR)Ku- and Ka-bands, and the GPM Microwave Imager. Eleven different nonspherical snowflake shapes were used in the analysis. Notable results include the following: 1) The W-band radar has detection thresholds more than an order of magnitude lower than the future GPM radars; 2) the cloud structure macrophysics influences the thresholds of detection for passive channels (e.g., snow events with larger ice water paths and thicker clouds are easier to detect); 3) the snowflake microphysics (mainly shape and density)plays a large role in the detection threshold for active and passive instruments; 4) with reasonable assumptions, the passive 166-GHz channel has detection threshold values comparable to those of the GPM DPR Ku- and Ka-band radars with approximately 0.05 g *m(exp -3) detected at the surface, or an approximately 0.5-1.0-mm * h(exp -1) melted snow rate. This paper provides information on the light snowfall events missed by the sensors and not captured in global estimates.

remote sensing↗
NASA NTRS2010

Community-Based Services that Facilitate Interoperability and Intercomparison of Precipitation Datasets from Multiple Sources

Over the past 12 years, large volumes of precipitation data have been generated from space-based observatories (e.g., TRMM), merging of data products (e.g., gridded 3B42), models (e.g., GMAO), climatologies (e.g., Chang SSM/I derived rain indices), field campaigns, and ground-based measuring stations. The science research, applications, and education communities have greatly benefited from the unrestricted availability of these data from the Goddard Earth Sciences Data and Information Services Center (GES DISC) and, in particular, the services tailored toward precipitation data access and usability. In addition, tools and services that are responsive to the expressed evolving needs of the precipitation data user communities have been developed at the Precipitation Data and Information Services Center (PDISC) (http://disc.gsfc.nasa.gov/precipitation or google NASA PDISC), located at the GES DISC, to provide users with quick data exploration and access capabilities. In recent years, data management and access services have become increasingly sophisticated, such that they now afford researchers, particularly those interested in multi-data set science analysis and/or data validation, the ability to homogenize data sets, in order to apply multi-variant, comparison, and evaluation functions. Included in these services is the ability to capture data quality and data provenance. These interoperability services can be directly applied to future data sets, such as those from the Global Precipitation Measurement (GPM) mission. This presentation describes the data sets and services at the PDISC that are currently used by precipitation science and applications researchers, and which will be enhanced in preparation for GPM and associated multi-sensor data research. Specifically, the GES-DISC Interactive Online Visualization ANd aNalysis Infrastructure (Giovanni) will be illustrated. Giovanni enables scientific exploration of Earth science data without researchers having to perform the complicated data access and match-up processes. In addition, PDISC tool and service capabilities being adapted for GPM data will be described, including the Google-like Mirador data search and access engine; semantic technology to help manage large amounts of multi-sensor data and their relationships; data access through various Web services (e.g., OPeNDAP, GDS, WMS, WCS); conversion to various formats (e.g., netCDF, HDF, KML (for Google Earth)); visualization and analysis of Level 2 data profiles and maps; parameter and spatial subsetting; time and temporal aggregation; regridding; data version control and provenance; continuous archive verification; and expertise in data-related standards and interoperability. The goal of providing these services is to further the progress towards a common framework by which data analysis/validation can be more easily accomplished.

Liu, Zhong↗
NASA NTRS2012

Detecting Falling Snow from Space

There is an increased interest in detecting and estimating the amount of falling snow reaching the Earth's surface in order to fully capture the atmospheric water cycle. An initial step toward global spaceborne falling snow algorithms includes determining the thresholds of detection for various active and passive sensor channel configurations, snow event cloud structures and microphysics, snowflake particle electromagnetic properties, and surface types. In this work, cloud resolving model simulations of a lake effect and synoptic snow event were used to determine the minimum amount of snow (threshold) that could be detected by the following instruments: the W -band radar of CloudSat, Global Precipitation Measurement (GPM) Dual-frequency Precipitation Radar (DPR) Ku and Ka band, and the GPM Microwave Imager (GMI) channels from 10 to 183 plus or minus 7 GHz. Eleven different snowflake shapes were used to compute radar reflectivities and passive brightness temperatures. Notable results include: (1) the W-Band radar has detection thresholds more than an order of magnitude lower than the future GPM sensors, (2) the cloud structure macrophysics influences the thresholds of detection for passive channels, (3) the snowflake microphysics plays a large role in the detection threshold for active and passive instruments, (4) with reasonable assumptions, "the passive 166 GHz channel has detection threshold values comparable to the GPM DPR Ku and Ka band radars with approximately 0.05 g per cubic meter detected at the surface, or an approximately 0.5-1 millimeter per hr. melted snow rate (equivalent to 0.5-2 centimeters per hr. solid fluffy snowflake rate). With detection levels of falling snow known, we can focus current and future retrieval efforts on detectable storms and concentrate advances on achievable results. We will also have an understanding of the light snowfall events missed by the sensors and not captured in the global estimates.

Gail Skofronick-Jackson↗
NASA NTRS2012

Tropical Rainfall Measuring Mission (TRMM) Precipitation Data and Services for Research and Applications

Precipitation is a critical component of the Earth's hydrological cycle. Launched on 27 November 1997, TRMM is a joint U.S.-Japan satellite mission to provide the first detailed and comprehensive data set of the four-dimensional distribution of rainfall and latent heating over vastly under-sampled tropical and subtropical oceans and continents (40 S - 40 N). Over the past 14 years, TRMM has been a major data source for meteorological, hydrological and other research and application activities around the world. The purpose of this short article is to inform that the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) provides TRMM archive and near-real-time precipitation data sets and services for research and applications. TRMM data consist of orbital data from TRMM instruments at the sensor s resolution, gridded data at a range of spatial and temporal resolutions, subsets, ground-based instrument data, and ancillary data. Data analysis, display, and delivery are facilitated by the following services: (1) Mirador (data search and access); (2) TOVAS (TRMM Online Visualization and Analysis System); (3) OPeNDAP (Open-source Project for a Network Data Access Protocol); (4) GrADS Data Server (GDS); and (5) Open Geospatial Consortium (OGC) Web Map Service (WMS) for the GIS community. Precipitation data application services are available to support a wide variety of applications around the world. Future plans include enhanced and new services to address data related issues from the user community. Meanwhile, the GES DISC is preparing for the Global Precipitation Measurement (GPM) mission which is scheduled for launch in 2014.

Liu, Zhong↗
NASA NTRS2005

Validation for the Tropical Rainfall Measuring Mission: Lessons Learned and Future Plans

The Tropical Rainfall Measuring Mission (TRMM) was launched in November 1997 and is a highly regarded and successful mission. A major component of the TRMM program was its Ground Validation (GV) program. Through dedicated research and hard work by many groups, both the GV and satellite-retrieved rain estimates have shown a convergence at key GV sites, lending credibility to the global TRMM estimates. To be sure, there are some regional differences between the various satellite estimates themselves, which still need to be addressed; however, it can be said with some certainty that TRMM has provided a high-quality, long-term climatological data set for researchers that provides errors on the order of 10-20%, rather than pre-TRMM era error estimates on the order of 50-100%. The TRMM GV program's main operational task is to provide rainfall products for four sites: Darwin, Australia (DARW); Houston, Texas (HSTN); Kwajalein, Republic of the Marshall Islands (KWAJ); and, Melbourne, Florida (MELB). A comparison between TRMM Ground Validation (Version 5) and Satellite (Version 6) rain intensity estimates is presented. The gridded satellite product (3668) will be compared to GV Level II rain-intensity and -type maps (2A53 and 2A54, respectively). The 3G68 product represents a 0.5 deg x 0.5 deg data grid providing estimates of rain intensities from the TRMM Precipitation Radar (PR), Microwave Imager (TMI) and Combined (COM) algorithms. The comparisons will be sub-setted according to geographical type (land, coast and ocean). The convergence of the GV and satellite estimates bodes well for expectations for the proposed Global Precipitation Measurement (GPM) program and this study and others are being leveraged towards planning GV goals for GPM. A discussion of lessons learned and future plans for TRMM GV in planning for GPM will also be provided.

Wolff, David B.↗