Changes in the TRMM Version-5 and Version-6 Precipitation Radar Products Due to Orbit Boost

The performance of the version-5 and version-6 Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR) products before and after the satellite orbit boost is assessed through a series of comparisons with Weather Surveillance Radar (WSR)-88D ground-based radar in Melbourne, Florida. Analysis of the comparisons of radar reflectivity near the storm top from the ground radar and both versions of the PR indicates that the PR bias relative to the WSR radar at Melbourne is on the order of 1dB for both pre- and post-boost periods, indicating that the PR products maintain accurate calibration after the orbit boost. Comparisons with the WSR-88D near-surface reflectivity factors indicate that both versions of the PR products accurately correct for attenuation in stratiform rain. However, in convective rain, both versions exhibit negative biases in the near-surface radar reflectivity with version-6 products having larger negative biases than version-5. Rain rate comparisons between the ground and space radars show similar characteristic

An Updated TRMM Composite Climatology of Tropical Rainfall and Its Validation

An updated 15-yr Tropical Rainfall Measuring Mission (TRMM) composite climatology (TCC) is presented and evaluated. This climatology is based on a combination of individual rainfall estimates made with data from the primaryTRMMinstruments: theTRMM Microwave Imager (TMI) and the precipitation radar (PR). This combination climatology of passive microwave retrievals, radar-based retrievals, and an algorithm using both instruments simultaneously provides a consensus TRMM-based estimate of mean precipitation. The dispersion of the three estimates, as indicated by the standard deviation sigma among the estimates, is presented as a measure of confidence in the final estimate and as an estimate of the uncertainty thereof. The procedures utilized by the compositing technique, including adjustments and quality-control measures, are described. The results give a mean value of the TCC of 4.3mm day(exp -1) for the deep tropical ocean beltbetween 10 deg N and 10 deg S, with lower values outside that band. In general, the TCC values confirm ocean estimates from the Global Precipitation Climatology Project (GPCP) analysis, which is based on passive microwave results adjusted for sampling by infrared-based estimates. The pattern of uncertainty estimates shown by sigma is seen to be useful to indicate variations in confidence. Examples include differences between the eastern and western portions of the Pacific Ocean and high values in coastal and mountainous areas. Comparison of the TCC values (and the input products) to gauge analyses over land indicates the value of the radar-based estimates (small biases) and the limitations of the passive microwave algorithm (relatively large biases). Comparison with surface gauge information from western Pacific Ocean atolls shows a negative bias (~16%) for all the TRMM products, although the representativeness of the atoll gauges of open-ocean rainfall is still in question

Developing GIOVANNI-based Online Prototypes to Intercompare TRMM-Related Global Gridded-Precipitation Products

New online prototypes have been developed to extend and enhance the previous effort by facilitating investigation of product characteristics and intercomparison of precipitation products in different algorithms as well as in different versions at different spatial scales ranging from local to global without downloading data and software. Several popular Tropical Rainfall Measuring Mission (TRMM) products and the TRMM Composite Climatology are included. In addition, users can download customized data in several popular formats for further analysis. Examples show product quality problems and differences in several monthly precipitation products. It is seen that differences in daily and monthly precipitation products are distributed unevenly in space and it is necessary to have tools such as those presented here for customized and detailed investigations. A simple time series and two area maps allow the discovery of abnormal values of 3A25 in one of the months. An example shows a V-shaped valley issue in the Version 6 3B43 time series and another example shows a sudden drop in 3A25 monthly rain rate, all of which provide important information when the products are used for long-term trend studies. Future plans include adding more products and statistical functionality in the prototypes

Parametric optimal estimation retrieval of the non-precipitating parameters over the global oceans, A

2006 Summer.Includes bibliographical references (pages 82-87).Covers not scanned.Print version deaccessioned 2021.There are a multitude of spacebome microwave sensors in orbit, including the TRMM Microwave Imager (TMI), the Special Sensor Microwave/lmager (SSM/I) onboard the DMSP satellites, the Advanced Microwave Scanning Radiometer - Earth Observing System (AMSR-E), SSMIS, WINDSAT, and others. Future missions, such as the planned Global Precipitation Measurement (GPM) Mission, will incorporate additional spacebome microwave sensors. The need for consistent geophysical parameter retrievals among an ever-increasing number of microwave sensors requires the development of a physical retrieval scheme independent of any particular sensor and flexible enough so that future microwave sensors can be added with relative ease. To this end, we attempt to develop a parametric retrieval algorithm currently applicable to the non-precipitating atmosphere with the goal of having consistent non-precipitating geophysical parameter products. An algorithm of this nature makes is easier to merge separate products, which, when combined, would allow for additional global sampling or longer time series of the retrieved global geophysical parameters for climate purposes. This algorithm is currently applied to TMI, SSM/I and AMSR-E with results that are comparable to other independent microwave retrievals of the non-precipitating parameters designed for specific sensors. The physical retrieval is developed within the optimal estimation framework. The development of the retrieval within this framework ensures that the simulated radiances corresponding to the retrieved geophysical parameters will always agree with observed radiances regardless of the sensor being used. Furthermore, a framework of this nature allows one to easily add additional physics to describe radiation propagation through raining scenes, thus allowing for the merger of cloud and precipitation retrievals, if so desired. Additionally, optimal estimation provides error estimates on the retrieval, a product often not available in other algorithms, information on potential forward model/sensor biases, and a number of useful diagnostics providing information on the validity and significance of the retrieval (such as Chi-Square, indicative of the general "fit" between the model and observations and the A-Matrix, indicating the sensitivity of the model to a change in the geophysical parameters). There is an expected global response of these diagnostics based on the scene being observed, such as in the case of a raining scene. Fortunately, since TRMM has a precipitation radar (TRMM PR) in addition to a radiometer (TMI) flying on-board, the expected response of the retrieval diagnostics to rainfall can be evaluated. It is shown that a potentially powerful rainfall screen can then be developed for use in passive microwave rainfall and cloud property retrieval algorithms with the possibility of discriminating between precipitating and nonprecipitating scenes, and further indicating the possible contamination of rainfall in cloud liquid water path microwave retrievals

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

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

Validation of TRMM Precipitation Radar Through Comparison of its Multi-Year Measurements to Ground-Based Radar

A procedure to accurately resample spaceborne and ground-based radar data is described, and then applied to the measurements taken from the Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR) and the ground-based Weather Surveillance Radar-1988 Doppler (WSR-88D or WSR) for the validation of the PR measurements and estimates. Through comparisons with the well-calibrated, non-attenuated WSR at Melbourne, Florida for the period 1998-2007, the calibration of the Precipitation Radar (PR) aboard the TRMM satellite is checked using measurements near the storm top. Analysis of the results indicates that the PR, after taking into account differences in radar reflectivity factors between the PR and WSR, has a small positive bias of 0.8 dB relative to the WSR, implying a soundness of the PR calibration in view of the uncertainties involved in the comparisons. Comparisons between the PR and WSR reflectivities are also made near the surface for evaluation of the attenuation-correction procedures used in the PR algorithms. It is found that the PR attenuation is accurately corrected in stratiform rain but is underestimated in convective rain, particularly in heavy rain. Tests of the PR estimates of rainfall rate are conducted through comparisons in the overlap area between the TRMM overpass and WSR scan. Analyses of the data are made both on a conditional basis, in which the instantaneous rain rates are compared only at those pixels where both the PR and WSR detect rain, and an unconditional basis, in which the area-averaged rain rates are estimated independently for the PR and WSR. Results of the conditional rain comparisons show that the PR-derived rain is about 9% greater and 19% less than the WSR estimates for stratiform and convective storms, respectively. Overall, the PR tends to underestimate the conditional mean rain rate by 8% for all rain categories, a finding that conforms to the results of the area-averaged rain (unconditional) comparisons

Development of a new global rain model for radio regulation

Signal attenuation due to rain scatter is the dominant fade mechanism on the majority of high-capacity microwave telecommunications links, both terrestrial and Earth-space. These links carry a large proportion of the information that underpins the way modern life functions and is a vital component of national infrastructure. Many studies have established the virtuous cycle that exists between the development of telecommunications infrastructure and economic growth. Therefore, it is important that rain fade models exist for the design and optimisation of telecommunications networks, globally, but especially in developing countries.A set of internationally recognised and agreed radio propagation models is maintained by the International Telecommunications Union - Radiocommunication Sector (ITU-R) in the form of Recommendations. A fundamental input parameter to many of these models is the point one-minute rain rate exceeded for 0.01% (about 50 minutes) of an average year. Historically, the collection of one-minute rain rates has been rare and so very few regions of the world have measured this important parameter. Where local data are not available, the full distribution of one-minute rain rates, including the 0.01% exceeded rate, can be obtained from Rec. ITU-R P.837-7. The input parameters to this Recommendation are the average monthly temperatures and rain accumulations.The network of meteorological stations is very sparse in equatorial developing countries. This limits the reliability of monthly rain accumulation statistics. ITU-R models are validated against DBSG3: the database of link and meteorological measurements maintained by ITU-R Study Group 3. However, there is very little data from the Tropics in DBSG3. Therefore, there are legitimate concerns that the ITU-R P.837-7 model may not work accurately in the Tropics.This thesis uses rain rates derived from the satellite Earth observation Tropical Rain Measuring Mission, TRMM, to estimate point one-minute rain rate distributions in the Tropics. Two distinct uses of these data have been tested. Initially, the measured distributions of TRMM rain rates were used to estimate rain distributions in the Tropics. A method was developed to transform TRMM rain rate distributions to those needed for radio systems, based on UK rain radar data. In many cases, this method performed better than Rec. ITU-R P.837-7, particularly with databases of rain rates not included in DBSG3. To extend the work to global application, TRMM data were used to estimate the monthly rain rate distributions conditional upon monthly temperature and accumulation, as used in Rec. ITU-R P.837-7. These were then used to replace the analytic distributions in the Recommendation. The method worked well on several databases of measurements, but appeared to be biased in temperate regions. The measured TRMM conditional distributions were replaced by curve-fit approximations and a hybrid method was developed that combined the standard Rec. ITU-R P.837-7 prediction with the curve-fit TRMM prediction. This algorithm performed as well as or better than Rec. ITU-R P.837-7 for most test databases and at most time percentages.The direct use of satellite Earth observation data to produce distributions of point one-minute rain rates is a radical departure from methods used before. This thesis has shown the potential of satellite-based measurements to replace the current methods based on downscaling numerical weather prediction output. In the future when more satellite data are available, spanning the globe, this suggests that direct use of satellite data will become standard

Master of Science

thesisTropical Measuring Mission (TRMM) precipitation radar (PR) 2A25 V7 retrievals show that precipitation features (PFs) with the tallest maximum 40 dBZ echo heights are rarely the same PFs that exhibit the most extreme near-surface rainfall rates. To investigate the impacts of potential weaknesses in the 2A25 V7 retrievals, due to potential attenuation of the Ku-band signal, 14 years of June-August retrievals are compared to June-August 2014 hourly Weather Surveillance Radar - 1988 Doppler (WSR-88D) dual-polarimetric S-band data for 28 radars over the southeastern United States (SEUS). For more direct comparison, TRMM Ku-band measurements are converted to S-band approximations. Rain rates for WSR-88D data are approximated using the CSU-HIDRO algorithm, which calculates rain rates from reflectivity (Z), differential reflectivity (ZDR), and specific differential phase (KDP) relationships. This research aims to not only investigate the difference between TRMM PR and WSR-88D retrievals of reflectivity and rain rate, but also to investigate how apparent differences relate to TRMM path integrated attenuation (PIA), and WSR-88D KDP, ZDR and calculated hail fraction. Tropics-wide TRMM retrievals confirm previous findings of a low overlap of extreme convective intensity and extreme near-surface rain rates. WSR-88D data also confirm that this overlap is low, but show that it is likely higher than TRMM PR retrievals indicate, approximately 30% higher in the SEUS for the 99th percentiles of maximum 40 dBZ heights and low-level rain rates. For maximum 40 dBZ echo heights that extend into regions likely containing ice, mean WSR-88D reflectivities are approximately 2 dBZ higher than TRMM PR reflectivities. Higher WSR-88D-retrieved rain rates for a given low-level reflectivity combine with these higher low-level reflectivities to produce much greater mean WSR-88D rainfall rates than TRMM PR as a function of maximum 40 dBZ height, for heights that extend into regions likely to have ice. Investigations of TRMM PR PIA, and WSR-88D KDP, ZDR, and hail fraction indicate that the TRMM PR 2A25 V7 algorithm is possibly misidentifying low-mid level hail or graupel as greater attenuating liquid, or vice versa. This possible misidentification results in 2A25 V7 Z-R relationships that likely produce low biased rain rates in intense convection