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NASA NTRS2000

Evolution of South China Sea Summer Monsoon During SCSMEX-98: An Application of TRMM Data for Regional Hydro-Climate Studies

Evolution of South China Sea (SCS) summer monsoon in May-June, 1998 is investigated by using NASA/Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) and Precipitation Radar (PR) data and the SCS Monsoon Experiment (SCSMEX) data. The five-day mean moisture budget over the SCS region, and TMI surface rain rate, winds and divergence are calculated for the periods of pre-monsoon, onset, mature, and break. Results show that the SCS monsoon onset is triggered by the southward-propagating mid-latitude frontal system and the eastward-propagating intraseasonal oscillations. The disastrous flooding over the Yangtze River Basin in 1998 is caused mainly by the massive moisture transport by the lower-tropospheric prevailed westerly winds associated with the depression over the Bay of Bengal. The TRMM PR data are used to calculate the vertical distribution of fractional cover of Corrected Z-factor. Before the onset, the fractional cover 1-2% of 20-30 dBz appears around 2 km, indicating marine status clouds, During the monsoon onset and mature, the factional cover 34% of 25-35 dBz occurs below 6 km, indicating strong convection. The factional cover 5% of 20 dBz is around 8 km, which is indicative of large stratiform ice clouds. Yangtze River (YR) floods occurred as a part of the evolution of the East Asian summer monsoon. The rain rate over the YR shows out of phase with rainfall over the SCS. The vertical structures and statistical properties of clouds over the YR are compared with those over the SCS.

Lau, K.-M.↗
NASA NTRS2001

Gridded Hourly Text Products: A TRMM Data Reduction Approach

The quantity of precipitation data from satellite-based observations is a blessing and a curse. The sheer volume of the data makes it difficult for many researchers to use in targeted applications. This volume increases further as algorithm improvements lead to the reprocessing of mission data. In addition to the overall volume of data, the size and format complexity of orbital granules contribute to the difficulty in using all the available data. Finally, the number of different instruments available to measure rainfall and related parameters further contributes to the volume concerns. In summary, we have an embarrassment of riches. The science team of the Tropical Rainfall Measuring Mission (TRMM) recognized this dilemma and has developed a strategy to address it. The TRMM Science Data and Information System (TSDIS) produces, at the direction of the Joint TRMM Science Team, a number of instantaneous rainfall products. The TRMM Microwave Imager (TMI), the Precipitation Radar and a Combined TMI/PR are the key "instruments" used in this production. Each of these products contains an entire orbit of data. The algorithm code computes not just rain rates but a large number of other physical parameters as well as information needed for monitoring algorithm performance. That makes these products very large. For example, a single orbit of TMI rain rate product is 99 MB, a single orbit of the combined product yields a granule that is 158 MB, while the 80 vertical levels of rain information from the PR yields an orbital product of 253 MB. These are large products that are often difficult for science users to electronically transfer to their sites especially if they want a large period of time. Level 3 gridded products are much smaller, but their 5 or 30 day temporal resolution is insufficient for many researchers. In addition, TRMM standard products are produced in the HDF format. While a large number of user-friendly tools are available to hide the details of the format (including a toolkit developed at TSDIS for the TRMM science team), many potential users shy away

Stocker, Erich↗
NASA NTRS2001

Overview and Scientific Agenda of Global Precipitation Mission

This paper addresses the status of the Global Precipitation Mission (GPM) currently planned for launch in the 2007-2008 time frame. The GPM notional design involves a 9-member satellite constellation, one of which wilt be an advanced TRMM-like "core" satellite carrying a dual-frequency Ku-Ka band radar (DFPR) and a TMI-like radiometer. The other eight members of the constellation will be considered daughters of the core satellite, each carrying some type of passive microwave radiometer measuring across the 10.7 - 85 GHz ,frequency range - likely to include a combination of lightweight satellites and co-existing operational/Experimental satellites carrying passive microwave radiometers (i.e., SSM/I and AMSR-E & -F). The constellation is designed to provide no worse than 3-hour sampling at any spot on the globe using sun-synchronous orbit architecture for the daughter satellites, with the core satellite providing relevant measurements on internal cloud-precipitation microphysical processes and the "training-calibrating" information for retrieval algorithms used on daughter satellite measurements. The GPM is organized internationally, currently involving a partnership between NASA in the US, NASDA in Japan, and ESA in Europe (representing the European community nations). The mission is expected to involve additional international participants, sister agencies to the mainstream space agencies, and a diverse collection scientists from academia, government, and the private sector, A critical element in understanding the scientific thinking which has motivated the GPM project is an understanding of what scientific problems TRMM has and has not been able to address and at what scales. The TRMM satellite broke important scientific ground because it carried to space an array of rain-sensitive instruments, two of which were specifically designed for physical precipitation retrieval. These were the 9-channel TRMM Microwave Imager (TMI) and the 13.8 GHz Precipitation Radar (PR). By the same token, because TRMM is a single satellite in a low inclination, low altitude, non-sun-synchronous orbit, it cannot provide global coverage or regular diurnal sampling. These features are essential for many current scientific inquiries involving physical processes of climate and the global water cycle, the modeling of hydrometeorological-biogeochemical cycling, and coupled land-atmosphere/ocean-atmosphere exchanges. Moreover, TRMM has not been able to retrieve explicit properties of the drop size distribution (DSD), a final major barrier to making accurate rain measurements, because the single frequency TRMM radar cannot measure differential reflectivity. which is a minimal requirement for attacking rain retrieval within the framework of extinction cross-section-dependency. GPM is expected to surmount much of the DSD retrieval problem because its core satellite wilt have the capacity to make differential reflectivity measurements with its Ku-Ka band radar (13.6 - 35 GHz) called DFPR - being developed by NASDA/CRL in Japan. This paper will provide an overview of the above issues as well as present a discussion on the expected measurement improvements.

Smith, Eric A.↗
NASA NTRS2001

TRMM Data Assimilation at NASA

We give an overview of the research at NASA in assimilating tropical rainfall and total precipitable water (TPW) retrievals derived from the TRMM Microwave Imager (TMI) and the Special Sensor Microwave/ Imager (SSM/I) instruments. Global analyses currently contain order-one errors in the primary fields of the hydrological cycle such as precipitation, evaporation, moisture, and the related cloud fields, especially in the tropics. We show that an effective strategy to assimilate tropical rainfall data is to use observations to compensate for errors in moisture tendencies produced by the assimilation model. Results show that assimilating TMI and SSM/I surface rainrates and TPW estimates improves shortrange forecasts and reduces state-dependent systematic errors in the hydrological cycle and related climate parameters such as cloud, radiation, and the large-scale circulation in the tropics. The study provides a demonstration of the potential of using rainfall and moisture observations derived from passive microwave instruments to improve the quality of 4-dimensional global datasets for climate analysis and weather forecasting applications.

Hou, Arthur Y.↗
NASA NTRS2001

Validation of TRMM Satellite Rainfall Products Over Oklahoma for a Three Year Period (1998-2000)

This study evaluates the level 2 and level 3 rainfall products from the Tropical Rainfall Measuring Mission gridded distribution of 118 high resolution tipping buckets from the Oklahoma Mesonet. The Tropical Rainfall Measuring Mission, a joint satellite mission between NASA and the National Space Development Agency (NASDA) of Japan, was designed to estimate global precipitation between 40 S and 40 N latitude. The TRMM satellite, consisting of two main precipitation sensors, a passive microwave (TMI) and precipitation radar (PR) sensors, was launched in 1997. Although the great advantage of TRMM has been its ability to sample precipitation globally over the tropical oceans in places where ground sensors do not exist, regional points over land still offer the best opportunity for validating these rain estimates. For this study, the gauge data was gridded at I x I degree resolution between 34 and 36 N and -95 and -100 W. The location of Oklahoma is somewhat unique, in that, it is located near the turning point for the satellite. This study investigates effects of temporal sampling on the satellite measurements and the resulting rainfall bias observed between space and ground based sensors. The first part of the study analyzes the monthly rainfall statistics of the 3AI2 (TMI), the 3A25 (PR) and the 31142 (combined sensors) with the rain gauges. It uses regression techniques and a computation of the probability density function (PDF) and cumulative probability density function (CDF) for each sensor to evaluate the effects of temporal sampling on the satellite products and the sufficiency of using two overpasses a day to estimate rainfall on a monthly scale. In further probing this issue, the study also looked at the direct coincidence of ground and satellite measurements by comparing instantaneous comparisons obtained from the level 2 orbital track data, e.g. 2A12, 2A25. Lastly, the effects of temporal sampling were further studied by sub-sampling the gauge data only at overpass times. This methodology provides a quantitative way of separately inferring the fraction of bias due to temporal sampling and the rainfall algorithm. In addition to showing the salient effects of temporal sampling, the study also revisits the historical problem of correlating point gauge estimates with area estimates, though, the problem is ameliorated by averaging and gridding the data to the area resolution scale of the satellite.

Fisher, Brad↗
NASA NTRS2001

TRMM Precipitation Radar and Microwave Imager Observations of Convective and Stratiform Rain Over Land and Their Theoretical Implications

Observations of brightness temperature, Tb made over land regions by the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) radiometer have been analyzed along with the nearly simultaneous measurements of the vertical profiles of reflectivity factor, Z, made by the Precipitation Radar (PR) onboard the TRMM satellite. This analysis is performed to explore the interrelationship between the TMI and PR data in areas that are covered predominantly by convective or stratiform rain. In particular, we have compared on a scale of 20 km, average vertical profiles of Z with the averages of Tbs in the 19, 37 and 85 GHz channels. Generally, we find from these data that as Z increases, Tbs in the three channels decrease due to extinction. In order to explain physically the relationship between the Tb and Z observations, we have performed radiative transfer simulations utilizing vertical profiles of hydrometeors applicable to convective and stratiform rain regions. These profiles are constructed taking guidance from the Z observations of PR and recent LDR and ZDR measurements made by land-based polarimetric radars.

Prabhakara, C.↗
NASA NTRS2002

Effects of Uncertainty in TRMM Precipitation Radar Path Integrated Attenuation on Interannual Variations of Tropical Oceanic Rainfall

Considerable uncertainty surrounds the issue of whether precipitation over the tropical oceans (30 deg N/S) systematically changes with interannual sea-surface temperature (SST) anomalies that accompany El Nino (warm) and La Nina (cold) events. Time series of rainfall estimates from the Tropical Rainfall Measuring Mission (TRMM Precipitation Radar (PR) over the tropical oceans show marked differences with estimates from two TRMM Microwave Imager (TMI) passive microwave algorithms. We show that path-integrated attenuation derived from the effects of precipitation on the radar return from the ocean surface exhibits interannual variability that agrees closely with the TMI time series. Further analysis of the frequency distribution of PR (2A25 product) rain rates suggests that the algorithm incorporates the attenuation measurement in a very conservative fashion so as to optimize the instantaneous rain rates. Such an optimization appears to come at the expense of monitoring interannual climate variability.

Robertson, Franklin R.↗
NASA NTRS2003

Interannual Variability of the Tropical Water Cycle: Capabilities in the TRMM Era and Challenges for GPM

Considerable uncertainty surrounds the issue of whether precipitation over the tropical oceans (30" NE) systematically changes with interannual sea-surface temperature (SST) anomalies that accompany El Nino (warm) and La Nina (cold) events. Although it is well documented that El Nino-Southern Oscillation (ENSO) events with marked SST changes over the tropical oceans, produce significant regional changes in precipitation, water vapor, and radiative fluxes in the tropics, we still cannot yet adequately quantify the associated net integrated changes to water and heat balance over the entire tropical oceanic or land sectors. Robertson et al., [2001 GRL] for example, showed that substantial disagreement exists among contemporary satellite estimates of interannual variations in tropical rainfall that are associated with SST changes. Berg et al., [2002 J. Climate] have documented the distinct differences between precipitation structure over the eastern and western Pacific ITCZ and noted how various satellite precipitation algorithms may respond quite differently to ENSO modulations of these precipitation regimes. Resolving this uncertainty is important since precipitation and latent heat release variations over land and ocean sectors are key components of the tropical heat balance in its most aggregated form. Rainfall estimates from the Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR) averaged over the tropical oceans have not solved this issue and, in fact, show marked differences with estimates from two TRMM Microwave Imager (TMI) passive microwave algorithms. In this paper we will focus on findings that uncertainties in microphysical assumptions necessitated by the single-frequency PR measurement pose difficulties for detecting climate-related precipitation signals. Recent work has shown that path-integrated attenuation derived from the effects of precipitation on the radar return from the ocean surface exhibits interannual variability that agrees closely with the TMI time series, yet the PR rainfall interannual variability (and attenuation derived predominantly from reflectivity) differs even in sign. We will explore these apparent inconsistencies and detail their impact on estimates of how ENSO events perturb the tropical rainfall. We will place these results in perspective by considering requirements for precipitation accuracy for global climate variability and change studies involving ENSO, monsoon dynamics and variations, and climate model improvement and validation. The discussion will conclude with an assessment of the implications of these findings for Global Precipitation Mission (GPM) requirements.

Robertson, Franklin R.↗
NASA NTRS2003

Assimilation of Precipitation Information Retrieved from Passive Microwave Sensors

Estimates of surface precipitation and the associated vertical latent heating structure can be retrieved from space-borne passive microwave sensors such as the TRMM Microwave Imager (TMI) and Special Sensor Microwave/Imager (SSM/I). Studies have shown that assimilation of microwave rain rates can improve the quality of global analysis and forecast. Numerical weather prediction (NWP) centers are beginning to make operational use of these data. In the next few years, there will be a gradual increase in microwave rain products available from operational and research satellites, culminating to a target constellation of 9 satellites to provide global rain measurements every 3 hours with the proposed Global Precipitation Measurement (GPM) mission to be launched around 2007. Realizing the full potential of these observations in data assimilation will require continued advances in retrieval algorithms, assimilation techniques, and model physics. I present results from two research efforts at NASA GSFC. The first is on the assimilation of tropical precipitation using temperature/moisture tendency corrections within a continuous variational assimilation framework to improve analyses and forecasts produced by the Goddard Earth Observing System (GEOS) global data assimilation system. Results show that assimilation of TMI and SSM/I rain rates improves not only precipitation and moisture, but also the related clouds, radiation energy fluxes, and large-scale circulations in GEOS analyses. The improved analyses also yield better short-range forecasts, quantitative precipitation forecast (QPF) threat scores, and storm track predictions. The second part consists of results from exploratory experiments on variational assimilation of convective and stratiform latent heating profiles within the general framework of model parameter estimation as a way to identify systematic model errors and optimize physical paremeterization schemes in global forecast and climate models.

Hou, Arthur Y.↗
NASA NTRS2003

Effects of Uncertainty in TRMM Precipitation Radar Path Integrated Attenuation on Interannual Variations of Tropical Oceanic Rainfall

Considerable uncertainty surrounds the issue of whether precipitation over the tropical oceans (30 deg N/S) systematically changes with interannual sea-surface temperature (SST) anomalies that accompany El Nino (warm) and La Nina (cold) events. Time series of rainfall estimates from the Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR) over the tropical oceans show marked differences with estimates from two TRMM Microwave Imager (TMI) passive microwave algorithms. We show that path-integrated attenuation derived from the effects of precipitation on the radar return from the ocean surface exhibits interannual variability that agrees closely with the TMI time series. Our analysis of discrepancies between the PR rainfall and attenuation suggests that uncertainty in the assumed drop size distribution and associated attenuation/reflectivity/rainfall relationships inherent in single-frequency radar methods is a serious issue for studies of interannual variability.

Robertson, Franklin R.↗
NASA NTRS2004

Polarimetric Scanning Radiometer C and X Band Microwave Observations During SMEX03

Soil Moisture Experiments 2003 (SMEX03) was the second in a series of field campaigns using the NOAA Polarimetric Scanning Radiometer (PSR/CX) designed to validate brightness temperature data and soil moisture retrieval algorithms for the Advanced during SMEX03 were: calibration and validation of AMSR-E brightness temperature observations over different climate/vegetation regions of the US. (Alabama, Georgia, Oklahoma), identification of possible sources of Radio Frequency Interference (RFI), comparison of X-band observations from TRMM Microwave Imager (TMI), AMSR-E and PSR/CX, and exploring the potential of soil moisture retrieval algorithms using C and X band imagery in diverse landscapes. In the current investigation, more than one hundred flightlines of PSR/CX data were extensively processed to produce gridded brightness temperature products for the four study regions. Variations associated with soil moisture were not as large as hoped for due to the lack of significant rainfall in Oklahoma. Observations obtained over Alabama include a wide range of soil moisture and vegetation conditions for C and X band frequencies. These results clearly showed a lack of sensitivity to rainfall/soil moisture under forest canopy cover. Quantitative comparisons made between the PSR/CX, AMSR-E for validated that both the PSR/CX and AMSR-E data were well calibrated. X band comparisons of the PSR/CX high resolution and AMSR-E and TMI low-resolution data indicated a linear scaling for the range of conditions studied in SMEX03. These results will form the basis for further soil moisture investigations.

Jackson, Thomas J.↗
NASA NTRS2004

Precipitation and Latent Heating Distributions from Satellite Passive Microwave Radiometry: Method and Uncertainties - Part 1

A revised Bayesian algorithm for estimating surface rain rate, convective rain proportion, and latent heating/drying profiles from satellite-borne passive microwave radiometer observations over ocean backgrounds is described. The algorithm searches a large database of cloud-radiative model simulations to find cloud profiles that are radiatively consistent with a given set of microwave radiance measurements. The properties of these radiatively consistent profiles are then composited to obtain best estimates of the observed properties. The revised algorithm is supported by an expanded and more physically consistent database of cloud-radiative model simulations. The algorithm also features a better quantification of the convective and non-convective contributions to total rainfall, a new geographic database, and an improved representation of background radiances in rain-free regions. Bias and random error estimates are derived from applications of the algorithm to synthetic radiance data, based upon a subset of cloud resolving model simulations, and from the Bayesian formulation itself. Synthetic rain rate and latent heating estimates exhibit a trend of high (low) bias for low (high) retrieved values. The Bayesian estimates of random error are propagated to represent errors at coarser time and space resolutions, based upon applications of the algorithm to TRMM Microwave Imager (TMI) data. Errors in instantaneous rain rate estimates at 0.5 deg resolution range from approximately 50% at 1 mm/h to 20% at 14 mm/h. These errors represent about 70-90% of the mean random deviation between collocated passive microwave and spaceborne radar rain rate estimates. The cumulative algorithm error in TMI estimates at monthly, 2.5 deg resolution is relatively small (less than 6% at 5 mm/day) compared to the random error due to infrequent satellite temporal sampling (8-35% at the same rain rate).

Olson, William S.↗
NASA NTRS2005

Interannual Variability of Tropical Rainfall as Seen From TRMM

Considerable uncertainty surrounds the issue of whether precipitation over the tropical oceans (30deg N/S) systematically changes with interannual sea-surface temperature (SST) anomalies that accompany El Nino (warm) and La Nina (cold) events. Although it is well documented that El Nino-Southern Oscillation (ENSO) events with marked SST changes over the tropical oceans produce significant regional changes in precipitation, water vapor, and radiative fluxes in the tropics, we still cannot yet adequately quantify the associated net integrated changes to water and heat balance over the entire tropical oceanic or land sectors. Resolving this uncertainty is important since precipitation and latent heat release variations over land and ocean sectors are key components of the tropical heat balance in its most aggregated form. Rainfall estimates from the Version 5 Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR) averaged over the tropical oceans have not solved this issue and, in fact, show marked differences with estimates from two TRMM Microwave Imager (TMI) passive microwave algorithms. In this paper we will focus on findings that uncertainties in microphysical assumptions necessitated by the single-frequency PR measurement pose difficulties for detecting climate-related precipitation signals. Recent work has shown that path-integrated attenuation derived from the effects of precipitation on the radar return from the ocean surface exhibits interannual variability that agrees closely with the TMI time series, yet the PR rainfall interannual variability (and attenuation derived predominantly from reflectivity) differs even in sign. We will explore these apparent inconsistencies and examine changes in new TRMM Version 6 retrievals. To place these results in a tropical water balance perspective we also examine interannual variations in evaporation over the tropical oceans made from TRMM and SSM/I (Special Sensor Microwave Imager) measurements of surface winds and humidity. Evaporation estimates from reanalysis and several global model experiments will also be compared to the TRMM findings and evaluated for consistency. The ability to detect regional shifts in freshwater flux over the oceans (equivalently, integrated moisture convergence) and moisture transport will be discussed.

Robertson, Franklin R.↗
NASA NTRS2006

Inter-calibrating Brightness Temperatures of a Constellation of Radiometers

In 2010, the National Aeronautics and Space Agency (NASA) of the U.S.A. and the Japanese Exploration Agency (JAXA) of Japan in cooperation with other U.S. and international partners will launch the Global Precipitation Measurement (GPM) mission. The mission center-piece is a core U.S. provided satellite holding a scanning microwave imager provided by the U.S. A. and a dual-frequency precipitation radar provided by Japan. The core satellite is in a 65 deg inclination (the current Tropical Rainfall Measuring Mission -TRMM is in a 35 deg inclination). Joining the core satellite are a constellation of approximately 8 satellites containing scanning radiometers. The purpose of the constellation is to increase the global sampling capability of the mission. One constellation satellite will be provided by NASA. Some of the constellation radiometers will provided by other U.S. agencies with existing (e.g. SSMI/S) or planned (NPOESS) radiometer assets. International groups have also expressed interest in contributing to the GPM mission including providing radiometer data for the GPM constellation. The use of a heterogeneous group of scanning radiometers each with its own unique purpose, characteristics and calibration offers a significant challenge for combining brightness temperatures or rain retrievals to create meaningful combined global radiometer products. However, the availability of active dual precipitation radar on the GPM core in combination with a well-calibrated radiometer on the same platform offers the possibility of inter-calibrating the constellation radiometers using the core satellite as a calibrator. This paper describes a joint NASA/GSFC and Colorado State University prototype effort at inter-calibrating existing radiometers using such a core calibrator approach. In the prototype, existing radiometers (i.e. SSM/I and AMSR-E) are intercalibrated, as required, using the TRMM Precipitation Radar (PR) and TRMM Microwave Imager (TMI) as the calibration core. This prototype includes four basic steps: provide the data in a normalized-common logical radiometer format, convert all data to a granule that stops and starts at the southernmost latitude rather than at the equator, perform basic processing and finally inter-calibrate the radiometers with the TRMM core as appropriate. Basic processing includes quality control, cross-track bias correction as well as the core action of converting T(sub a), to T(sub b). The purpose of the inter-calibration step is to provide consistent brightness temperatures for the various SSMA sensors, TMI and AMSR-E. The C appended to the level of the product indicates common brightness temperature (T(sub c)). The paper provides a summary of each of these actions. The details of the processing as well as data format and tools can be obtained by connecting to the website at mrain.atmos.colostate.eduLEVEL1C .

Stocker, Erich Franz↗
NASA NTRS2006

Diurnal Variability of Vertical Structure from a TRMM Passive Microwave "Virtual Radar" Retrieval

Robust description of the diurnal cycle from TRMM observations is complicated by the limitations of Low Earth Orbit (LEO) sampling; from a 'climatological' perspective, sufficient sampling must exist to control for both spatial and seasonal variability, before tackling an additional diurnal component (e.g., with 8 additional 3-hourly or 24 1-hourly bins). For documentation of vertical structure, the narrow sample swath of the TRMM Precipitation Radar limits the resolution of any of these components. A neural-network based 'virtual radar" retrieval has been trained and internally validated, using multifrequency / multipolarization passive microwave(TM1) brightness temperatures and textures parameters and lightning (LIS) observations, as inputs, and PR volumetric reflectivity as targets (outputs). By training the algorithms (essentially highly multivariate, nonlinear regressions) on a very large sample of high-quality co-located data from the center of the TRMM swath, 3D radar reflectivity and derived parameters (VIL, IWC, Echo Tops, etc.) can be retrieved across the entire TMI swath, good to 8-9% over the dynamic range of parameters. As a step in the retrieval (and as an output of the process), each TMI multifrequency pixel (at 85 GHz resolution) is classified into one of the 25 archetypal radar profile vertical structure "types", previously identified using cluster analysis. The dynamic range of retrieved vertical structure appears to have higher fidelity than the current (Version 6) experimental GPROF hydrometeor vertical structure retrievals. This is attributable to correct representation of the prior probabilities of vertical structure variability in the neural network training data, unlike the GPROF cloud-resolving model training dataset used in the V6 algorithms. The LIS lightning inputs are supplementary inputs, and a separate offline neural network has been trained to impute (predict) LIS lightning from passive-microwave-only data. The virtual radar retrieval is thus, in principle, extensible to Aqua/AMSR-E and NPOESS/CMIS passive microwave instruments. The virtual radar approach yields a threefold increase in effective sampling from the mission, albeit of lower-quality "retrieved" data, reducing the variance of local estimates by one third (or the standard deviation by-0.57). In this talk, the variance reduction is leveraged to more finely resolve global diurnal variability in both space and time (local hour).

Boccippio, Dennis J.↗
NASA NTRS2007

Detailed Analysis of Indian Summer Monsoon Rainfall Processes with Modern/High-Quality Satellite Observations

We examine, in detail, Indian Summer Monsoon rainfall processes using modernhigh quality satellite precipitation measurements. The focus here is on measurements derived from three NASA cloud and precipitation satellite missionslinstruments (TRMM/PR&TMI, AQUNAMSRE, and CLOUDSATICPR), and a fourth TRMM Project-generated multi-satellite precipitation measurement dataset (viz., TRMM standard algorithm 3b42) -- all from a period beginning in 1998 up to the present. It is emphasized that the 3b42 algorithm blends passive microwave (PMW) radiometer-based precipitation estimates from LEO satellites with infi-ared (IR) precipitation estimates from a world network of CEO satellites (representing -15% of the complete space-time coverage) All of these observations are first cross-calibrated to precipitation estimates taken from standard TRMM combined PR-TMI algorithm 2b31, and second adjusted at the large scale based on monthly-averaged rain-gage measurements. The blended approach takes advantage of direct estimates of precipitation from the PMW radiometerequipped LEO satellites -- but which suffer fi-om sampling limitations -- in combination with less accurate IR estimates from the optical-infrared imaging cameras on GEO satellites -- but which provide continuous diurnal sampling. The advantages of the current technologies are evident in the continuity and coverage properties inherent to the resultant precipitation datasets that have been an outgrowth of these stable measuring and retrieval technologies. There is a wealth of information contained in the current satellite measurements of precipitation regarding the salient precipitation properties of the Indian Summer Monsoon. Using different datasets obtained from the measuring systems noted above, we have analyzed the observations cast in the form of: (1) spatially distributed means and variances over the hierarchy of relevant time scales (hourly I diurnally, daily, monthly, seasonally I intra-seasonally, and inter-annually), (2) time series at these different time scales taken as area-averages over the hierarchy of relevant space scales (Indian sub-Division, Indian sub-continent, and Circumambient Indian Ocean), (3) principal autocorrelation and cross-correlation structures over various monsoon space-time domains, (4) diurnally modulated amplitude-phase properties of rain rates over different monsoon space-time domains, (5) foremost rain rate probability distributions intrinsic to monsoon precipitation, and (6) behavior of extreme events including occurrences of flood and drought episodes throughout the course of inter-annual monsoon processes.

Smith, Eric A.↗
NASA NTRS2008

Assessing the Relative Performance of Microwave-based Satellite Rain Rate Retrievals using TRMM Ground Validation Data

Space-borne microwave sensors provide critical rain information used in several global multi-satellite rain products, which in turn are used for a variety of important studies, including landslide forecasting, flash flood warning, data assimilation, climate studies, and validation of model forecast of precipitation. This study employs four years (2003-2006) of satellite data to assess the relative performance and skill of SSM/I (F13, F14 and F15), AMSU-B (N15, N16 and N17), AMSR-E (AQUA) and the TRMM Microwave Imager (TMI) in estimating surface rainfall based on direct instantaneous comparison with ground-based rain estimates from Tropical Rainfall Measuring Mission (TRMM) Ground Validation (GV) sites at Kwajalein, Republic of the Marshall Islands (KWAJ) and Melbourne, Florida (MELB). The relative performance of each of these satellites is examined via comparisons with GV radar-based rain rate estimates. Because underlying surface terrain is known to affect the relative performance of the satellite algorithms, the data for MELB was further stratified into ocean, land and coast categories using a 0.25 terrain mask. Of all the satellite estimates compared in this study, TMI and AMSR-E exhibited considerably higher correlations and skills in estimating/observing surface precipitation. While SSM/I and AMSU-B exhibited lower correlations and skills for each of the different terrain categories, the SSM/I absolute biases trended slightly lower than AMSRE over ocean, where the observations from both emission and scattering channels were used in the retrievals. AMSU-B exhibited the least skill relative to GV in all of the relevant statistical categories, and an anomalous spike was observed in the probability distribution functions near 1.0 mm hr-1. This statistical artifact appears to be related to attempts by algorithm developers to include some lighter rain rates, not easily detectable by its scatter-only frequencies. AMSU-B, however, agreed well with GV when the matching data was analyzed on monthly scales. These results signal developers of global rainfall products, such as the TRMM Multi-Satellite Precipitation Analysis (TMPA), and the Climate Data Center s Morphing (CMORPH) technique, that care must be taken when incorporating data from these input satellite estimates in order to provide the highest quality estimates in their products.

Wolff, David B.↗
NASA NTRS2010

Assessing the Relative Performance of Microwave-Based Satellite Rain Rate Retrievals Using TRMM Ground Validation Data

Space-borne microwave sensors provide critical rain information used in several global multi-satellite rain products, which in turn are used for a variety of important studies, including landslide forecasting, flash flood warning, data assimilation, climate studies, and validation of model forecasts of precipitation. This study employs four years (2003-2006) of satellite data to assess the relative performance and skill of SSM/I (F13, F14 and F15), AMSU-B (N15, N16 and N17), AMSR-E (Aqua) and the TRMM Microwave Imager (TMI) in estimating surface rainfall based on direct instantaneous comparisons with ground-based rain estimates from Tropical Rainfall Measuring Mission (TRMM) Ground Validation (GV) sites at Kwajalein, Republic of the Marshall Islands (KWAJ) and Melbourne, Florida (MELB). The relative performance of each of these satellite estimates is examined via comparisons with space- and time-coincident GV radar-based rain rate estimates. Because underlying surface terrain is known to affect the relative performance of the satellite algorithms, the data for MELB was further stratified into ocean, land and coast categories using a 0.25 terrain mask. Of all the satellite estimates compared in this study, TMI and AMSR-E exhibited considerably higher correlations and skills in estimating/observing surface precipitation. While SSM/I and AMSU-B exhibited lower correlations and skills for each of the different terrain categories, the SSM/I absolute biases trended slightly lower than AMSRE over ocean, where the observations from both emission and scattering channels were used in the retrievals. AMSU-B exhibited the least skill relative to GV in all of the relevant statistical categories, and an anomalous spike was observed in the probability distribution functions near 1.0 mm/hr. This statistical artifact appears to be related to attempts by algorithm developers to include some lighter rain rates, not easily detectable by its scatter-only frequencies. AMSU-B, however, agreed well with GV when the matching data was analyzed on monthly scales. These results signal developers of global rainfall products, such as the TRMM Multi-Satellite Precipitation Analysis (TMPA), and the Climate Data Center s Morphing (CMORPH) technique, that care must be taken when incorporating data from these input satellite estimates in order to provide the highest quality estimates in their products. 3

Wolff, David B.↗