On requirements for a satellite mission to measure tropical rainfall

Tropical rainfall data are crucial in determining the role of tropical latent heating in driving the circulation of the global atmosphere. Also, the data are particularly important for testing the realism of climate models, and their ability to simulate and predict climate accurately on the seasonal time scale. Other scientific issues such as the effects of El Nino on climate could be addressed with a reliable, extended time series of tropical rainfall observations. A passive microwave sensor is planned to provide information on the integrated column precipitation content, its areal distribution, and its intensity. An active microwave sensor (radar) will define the layer depth of the precipitation and provide information about the intensity of rain reaching the surface, the key to determining the latent heat input to the atmosphere. A visible/infrared sensor will provide very high resolution information on cloud coverage, type, and top temperatures and also serve as the link between these data and the long and virtually continuous coverage by the geosynchronous meteorological satellites. The unique combination of sensor wavelengths, coverages, and resolving capabilities together with the low-altitude, non-Sun synchronous orbit provide a sampling capability that should yield monthly precipitation amounts to a reasonable accuracy over a 500- by 500-km grid

A study of the structure of radar rainfall and its errors

Els objectius principals d’aquesta tesi són dos: d’una banda estudiar l’estructura de la variabilitat de la precipitació a diferents escales espacials i temporals, i de l’altra, estudiar l’estructura dels errors en les estimacions quantitatives de precipitació a través de radar. Pel que fa a l’estudi de l’estructura de la precipitació es proposa un marc de comparació per a mètodes de downscaling basat en valorar el grau amb què cada mètode és capaç de reproduir la variabilitat observada a les diferents escales de la pluja i la seva estructura multifractal. Finalment es proposa un mètode de downscaling tridimensional per a generar camps de precipitació d’alta resolució. Partint de dades mesurades amb radar, és capaç de reproduir la variabilitat a totes les escales de la pluja, i a la vegada, conservar l’estructura vertical de la precipitació observada pel radar. En aquesta tesi s’estudia també l’estructura dels errors associats a les mesures de radar, tant terrestre com embarcat en satèl·lit, que queden després de la cadena de correcció. Es realitza un estudi mitjançant simulació física de les observacions del radar, sobre un camp de precipitació d’alta resulució, per caracteritzar l’error relacionat amb la distància d’observació. També es caracteritza l’error total en les estimacions quantitatives de pluja dels radars terrestres mitjançant comparació contra un producte de referència basat en la combinació de radar i pluviòmetres. L’estructura de l’error trobada ha estat usada per generar un ensemble d’estimacions de pluja, que representa la incertesa en les estimacions, i pot ser emprat per aplicacions probabilístiques. Pel que fa a l’estudi de l’estructura de l’error associat a les estimacions de radar embarcat en satel·lit, s’han realitzat comparacions del radar embarcat en el satèl·lit TRMM contra equipament terrestre, per tal de caracteritzar, sota diverses condicions, les diferències en les mesures de precipitació.The principal objectives of this thesis are two: on one hand study the structure of the precipitation’s variability at different spatial and temporal scales, and on the other hand study the structure of the errors in the quantitative precipitation estimates by radar. In relation to the precipitation structure, a comparison framework for downscaling methods is proposed. Within this framework, the capability of each method reproducing the variability and multifractal behaviour observed in rainfall can be tested. A three-dimensional downscaling method to generate high-resolution precipitation fields from radar observations is proposed. The method is capable to reproduce the variability of rainfall at all scales and, at the same time, preserve the vertical structure of precipitation observed by the radar. In this thesis the structure of the errors that remain after the correction chain in radar measurements (both ground- and space-borne) is also studied. Simulation of the radar physical measurement process over high-resolution precipitation fields is performed to characterize the error related with range. The overall error in quantitative precipitation estimates by radar is characterized through comparison of radar estimates with a reference product based on a radar-raingauges merging. The error structure is used to generate a radar ensemble of precipitation estimates that represents the uncertainty in the measurements and can be used in probabilistic applications. Regarding the study of the errors associated to spaceborne radar measurements, comparisons of TRMM Precipitation Radar with ground equipment are performed to characterize the discrepancies between the precipitation estimates under different conditions

Integrated Multi-Satellite Evaluation for the Global Precipitation Measurement: Impact of Precipitation Types on Spaceborne Precipitation Estimation

Integrated multi-sensor assessment is proposed as a novel approach to advance satellite precipitation validation in order to provide users and algorithm developers with an assessment adequately coping with the varying performances of merged satellite precipitation estimates. Gridded precipitation rates retrieved from space sensors with quasi-global coverage feed numerous applications ranging from water budget studies to forecasting natural hazards caused by extreme events. Characterizing the error structure of satellite precipitation products is recognized as a major issue for the usefulness of these estimates. The Global Precipitation Measurement (GPM) mission aims at unifying precipitation measurements from a constellation of low-earth orbiting (LEO) sensors with various capabilities to detect, classify and quantify precipitation. They are used in combination with geostationary observations to provide gridded precipitation accumulations. The GPM Core Observatory satellite serves as a calibration reference for consistent precipitation retrieval algorithms across the constellation. The propagation of QPE uncertainty from LEO active/passive microwave (PMW) precipitation estimates to gridded QPE is addressed in this study, by focusing on the impact of precipitation typology on QPE from the Level-2 GPM Core Observatory Dual-frequency Precipitation Radar (DPR) to the Microwave Imager (GMI) to Level-3 IMERG precipitation over the Conterminous U.S. A high-resolution surface precipitation used as a consistent reference across scales is derived from the ground radar-based Multi-Radar/Multi-Sensor. While the error structure of the DPR, GMI and subsequent IMERG is complex because of the interaction of various error factors, systematic biases related to precipitation typology are consistently quantified across products. These biases display similar features across Level-2 and Level-3, highlighting the need to better resolve precipitation typology from space and the room for improvement in global-scale precipitation estimates. The integrated analysis and framework proposed herein applies more generally to precipitation estimates from sensors and error sources affecting low-earth orbiting satellites and derived gridded products

Proceedings of the Third Spaceborne Imaging Radar Symposium

This publication contains summaries of the papers presented at the Third Spaceborne Imaging Radar Symposium held at the Jet Propulsion Laboratory (JPL), California Institute of Technology, in Pasadena, California, on 18-21 Jan. 1993. The purpose of the symposium was to present an overview of recent developments in the different scientific and technological fields related to spaceborne imaging radars and to present future international plans. This symposium is the third in a series of 'Spaceborne Imaging Radar' symposia held at JPL. The first symposium was held in Jan. 1983 and the second in 1986

Use of Dual Polarization Radar in Validation of Satellite Precipitation Measurements: Rationale and Opportunities

Dual-polarization weather radars have evolved significantly in the last three decades culminating in the operational deployment by the National Weather Service. In addition to operational applications in the weather service, dual-polarization radars have shown significant potential in contributing to the research fields of ground based remote sensing of rainfall microphysics, study of precipitation evolution and hydrometeor classification. Furthermore the dual-polarization radars have also raised the awareness of radar system aspects such as calibration. Microphysical characterization of precipitation and quantitative precipitation estimation are important applications that are critical in the validation of satellite borne precipitation measurements and also serves as a valuable tool in algorithm development. This paper presents the important role played by dual-polarization radar in validating space borne precipitation measurements. Starting from a historical evolution, the various configurations of dual-polarization radar are presented. Examples of raindrop size distribution retrievals and hydrometeor type classification are discussed. The quantitative precipitation estimation is a product of direct relevance to space borne observations. During the TRMM program substantial advancement was made with ground based polarization radars specially collecting unique observations in the tropics which are noted. The scientific accomplishments of relevance to space borne measurements of precipitation are summarized. The potential of dual-polarization radars and opportunities in the era of global precipitation measurement mission is also discussed

Evaluation of Precipitation Estimates by at-Launch Codes of GPM/DPR Algorithms Using Synthetic Data from TRMM/PR Observations

The Global Precipitation Measurement (GPM) Core Observatory will carry a Dual-frequency Precipitation Radar (DPR) consisting of a Ku-band precipitation radar (KuPR) and a Ka-band precipitation radar (KaPR). In this study, \u27at-launch\u27 codes of DPR precipitation algorithms, which will be used in GPM ground systems at launch, were evaluated using synthetic data based upon the Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR) data. Results from the codes (Version 4.20131010) of the KuPR-only, KaPR-only, and DPR algorithms were compared with \u27true values\u27 calculated based upon drop size distributions assumed in the synthetic data and standard results from the TRMM algorithms at an altitude of 2 km over the ocean. The results indicate that the total precipitation amounts during April 2011 from the KuPR and DPR algorithms are similar to the true values, whereas the estimates from the KaPR data are underestimated. Moreover, the DPR estimates yielded smaller precipitation rates for rates less than about 10 mm/h and greater precipitation rates above 10 mm/h. Underestimation of the KaPR estimates was analyzed in terms of measured radar reflectivity ({\bf Z}-{\bf m}) of the KaPR at an altitude of 2 km. The underestimation of the KaPR data was most pronounced during strong precipitation events of {\bf Z}-{\bf m} \lt {\bf 18}~{\bf dBZ} (high attenuation cases) over heavy precipitation areas in the Tropics, whereas the underestimation was less pronounced when the {\bf Z}-{\bf m}\gt 26~{\bf dBZ} (moderate attenuation cases). The results suggest that the underestimation is caused by a problem in the attenuation correction method, which was verified by the improved codes

TRMM (Tropical Rainfall Measuring Mission): A satellite mission to measure tropical rainfall

The Tropical Rainfall Measuring Mission (TRMM) is presented. TRMM is a satellite program being studied jointly by the United States and Japan which would carry out the systematic study of tropical rainfall required for major strides in weather and climate research. The scientific justification for TRMM is discussed. The implementation process for the scientific community, NASA management, and the other decision-makers and advisory personnel who are expected to evaluate the priority of the project is outlined

Validating Precipitation Phase Measurements From Dual-Frequency Precipitation Radar On GPM Core Observatory Satellite

The purpose of this project is to validate precipitation measurements from the Global Precipitation Measurement (GPM) Core Observatory (GPM-CO) satellite. The GPM-CO satellite is being used to detect falling rain and snow. Being able to detect rain builds off the success of the Tropical Rainfall Measuring Mission (TRMM), which provided reasonable rainfall estimates when compared to ground-based radars. Detecting falling snow was a key GPM-CO requirement that was to be met within three years the satellite’s launch date of 27 February 2014. In this project, ground observations from Automated Surface Observing System (ASOS) and Automated Weather Observing Station (AWOS) was used to determine how well GPM-CO’s Dual-frequency Precipitation Radar (DPR) can detect and classify precipitation phase. If GPM can detect precipitation, especially snow, it could lead to increased knowledge of fresh water resources. GPM can lead to a better understanding of the full picture of the water cycle and the effects precipitation has on the availability of fresh water. This can result in identifying patterns of precipitation systems over land. Results show that DPR struggles to detect solid precipitation (snow), but if detected, then DPR successfully determines the phase. DPR detects liquid precipitation better than solid precipitation but does not do as well at classifying it. Results also show that performance is not as good over complex terrain. These are promising results as they show that GPM-CO satellite meets its requirement of detecting falling snow. Other results show that it is successful at detecting and classifying rainfall as well

Method to combine spaceborne radar and radiometric observations of precipitation, A

2010 Fall.Includes bibliographical references.This dissertation describes the development and application of a combined radar-radiometer rainfall retrieval algorithm for the Tropical Rainfall Measuring Mission (TRMM) satellite. A retrieval framework based upon optimal estimation theory is proposed wherein three parameters describing the raindrop size distribution (DSD), ice particle size distribution (PSD), and cloud water path (cLWP) are retrieved for each radar profile. The retrieved rainfall rate is found to be strongly sensitive to the a priori constraints in DSD and cLWP; thus, these parameters are tuned to match polarimetric radar estimates of rainfall near Kwajalein, Republic of Marshall Islands. An independent validation against gauge-tuned radar rainfall estimates at Melbourne, FL shows agreement within 2% which exceeds previous algorithms' ability to match rainfall at these two sites. The algorithm is then applied to two years of TRMM data over oceans to determine the sources of DSD variability. Three correlated sets of variables representing storm dynamics, background environment, and cloud microphysics are found to account for approximately 50% of the variability in the absolute and reflectivity-normalized median drop size. Structures of radar reflectivity are also identified and related to drop size, with these relationships being confirmed by ground-based polarimetric radar data from the North American Monsoon Experiment (NAME). Regional patterns of DSD and the sources of variability identified herein are also shown to be consistent with previous work documenting regional DSD properties. In particular, mid-latitude regions and tropical regions near land tend to have larger drops for a given reflectivity, whereas the smallest drops are found in the eastern Pacific Intertropical Convergence Zone. Due to properties of the DSD and rain water/cloud water partitioning that change with column water vapor, it is shown that increases in water vapor in a global warming scenario could lead to slight (1%) underestimates of a rainfall trends by radar but larger overestimates (5%) by radiometer algorithms. Further analyses are performed to compare tropical oceanic mean rainfall rates between the combined algorithm and other sources. The combined algorithm is 15% higher than the version 6 of the 2A25 radar-only algorithm and 6.6% higher than the Global Precipitation Climatology Project (GPCP) estimate for the same time-space domain. Despite being higher than these two sources, the combined total is not inconsistent with estimates of the other components of the energy budget given their uncertainties