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

TOWARDS IMPROVED QPE BY CAPITALIZING GROUND- AND SPACE- BASED PRECIPITATION MEASUREMENTS

There are primarily two types of weather radar systems offering precipitation measurements covering relatively large areas: (1) Ground-based Radar (GR) networks such as the NEXRAD and (2) Spaceborne radars onboard meteorological satellites. Ground-based polarimetric weather radar is arguably the most powerful validation tool that provides physical insight into the development and interpretation of space-borne weather radar algorithms and observations. To achieve the synergy between ground- and space-borne weather radar, this study first aims to compare and resolve discrepancies in hydrometeor retrievals and reflectivity observations between the NOAA/National Severe Storm Laboratory (NSSL) “proof of concept” polarimetric WSR-88D radar (KOUN) and the space-borne precipitation radar (PR) onboard NASA’s Tropical Rainfall Measuring Mission (TRMM) platform. The comparisons reveal an overall bias <0.2% between PR and KOUN. The bias is hypothesized to be from non-Raleigh scattering effects and/or errors in attenuation correction procedures applied to Ku-band PR measurements. Provided the upgrade of the U.S. national weather radar network to include polarimetric capabilities, the findings in this study will potentially serve as the basis for nation-wide validation of space precipitation products and also invite synergistic development of coordinated space/ground multisensor precipitation products. On the other hand, due to inadequate radar coverage from intervening terrain blockages, ground QPE needs enhancement aided by spaceborne radars. In the second part of the talk, I will introduce an approach that identifies and corrects for vertical profile of reflectivity (VPR) by using TRMM PR measurements in the region of Arizona and southern California, where ground-based NEXRAD radars are difficult to obtain reliable ground precipitation estimation due to complex terrain and limited radar coverage. A VPR Identification and Enhancement (VPR-IE) method based on the modeling of the vertical variations of the equivalent reflectivity factor using a physically-based parameterization and climatological information is employed to obtain VPRs at S-band from the TRMM PR measurement at Ku-band. The VPR-IE methodology is comprehensively evaluated with all stratiform precipitation events in cold season in the year of 2011. The results show that the VPR-IE has overall good performance and provides much more accurate surface rainfall estimates than original radar QPE in NMQ system. With the recent availability of GPM Dual-frequency PR, the VPR-IE approach is anticipated to be more robust and more useful by extending to higher latitude mountainous regions

Characterization of precipitation product errors across the United States using multiplicative triple collocation

Validation of precipitation estimates from various products is a challenging problem, since the true precipitation is unknown. However, with the increased availability of precipitation estimates from a wide range of instruments (satellite, ground-based radar, and gauge), it is now possible to apply the triple collocation (TC) technique to characterize the uncertainties in each of the products. Classical TC takes advantage of three collocated data products of the same variable and estimates the mean squared error of each, without requiring knowledge of the truth. In this study, triplets among NEXRAD-IV, TRMM 3B42RT, GPCP 1DD, and GPI products are used to quantify the associated spatial error characteristics across a central part of the continental US. Data are aggregated to biweekly accumulations from January 2002 through April 2014 across a 2° × 2° spatial grid. This is the first study of its kind to explore precipitation estimation errors using TC across the US. A multiplicative (logarithmic) error model is incorporated in the original TC formulation to relate the precipitation estimates to the unknown truth. For precipitation application, this is more realistic than the additive error model used in the original TC derivations, which is generally appropriate for existing applications such as in the case of wind vector components and soil moisture comparisons. This study provides error estimates of the precipitation products that can be incorporated into hydrological and meteorological models, especially those used in data assimilation. Physical interpretations of the error fields (related to topography, climate, etc.) are explored. The methodology presented in this study could be used to quantify the uncertainties associated with precipitation estimates from each of the constellations of GPM satellites. Such quantification is prerequisite to optimally merging these estimates

Inter-comparison of high-resolution satellite precipitation products over Central Asia

This paper examines the spatial error structures of eight precipitation estimates derived from four different satellite retrieval algorithms including TRMM Multi-satellite Precipitation Analysis (TMPA), Climate Prediction Center morphing technique (CMORPH), Global Satellite Mapping of Precipitation (GSMaP) and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN). All the original satellite and bias-corrected products of each algorithm (3B42RTV7 and 3B42V7, CMORPH_RAW and CMORPH_CRT, GSMaP_MVK and GSMaP_Gauge, PERSIANN_RAW and PERSIANN_CDR) are evaluated against ground-based Asian Precipitation-Highly Resolved Observational Data Integration Towards Evaluation of Water Resources (APHRODITE) over Central Asia for the period of 2004 to 2006. The analyses show that all products except PERSIANN exhibit overestimation over Aral Sea and its surrounding areas. The bias-correction improves the quality of the original satellite TMPA products and GSMaP significantly but slightly in CMORPH and PERSIANN over Central Asia. 3B42RTV7 overestimates precipitation significantly with large Relative Bias (RB) (128.17%) while GSMaP_Gauge shows consistent high correlation coefficient (CC) (>0.8) but RB fluctuates between -57.95% and 112.63%. The PERSIANN_CDR outperforms other products in winter with the highest CC (0.67). Both the satellite-only and gauge adjusted products have particularly poor performance in detecting rainfall events in terms of lower POD (less than 65%), CSI (less than 45%) and relatively high FAR (more than 35%)

Comparison And Evaluation Of Precipitation Products From Radar, Satellite, And Reanalyses Over The Continental United States

To better understand the precipitation variability over the continental United States (CONUS), an accurate temporally and spatially homogenous precipitation dataset should be used. Recently developed precipitation products, including satellite-based, radar-based, and atmospheric reanalysis products appear to fit these criteria, however, their uncertainties must first be addressed. This study is divided into two parts. Part I focuses on a comparison between satellite-based GPCP IDD estimates and radar-based NMQ Q2 estimates, offering physical insight into the differences between the two datasets. Part II evaluates the precipitation estimates from five reanalysis products, and studies the precipitation trend over the CONUS over the last three decades using GPCP monthly product, where the uncertainties associated with GPCP datasets found in part I will be addressed. In part I of this study, spatial averages of monthly and yearly accumulated precipitation were computed based on daily estimates from the six selected regions during the period from 2010 through 2012. Correlation coefficients for daily estimates over the selected regions range from 0.355 to 0.516 with mean differences (GPCP-Q2) varying from -0.86 to 0.99 mm. Better agreements are found in monthly estimates with the correlations varying from 0.635 to 0.787. The comparisons between two datasets are also conducted for warm (April-September) and cold (October-March) seasons. During the warm season, GPCP estimates are 9.7% less than Q2 estimates, while during the cold season GPCP estimates exceed Q2 estimates by 6.9%. For precipitation over the CONUS, although annual means are close (978.54 mm for Q2 vs. 941.79 mm for GPCP), Q2 estimates are much higher than GPCP over the central and southern CONUS and lower than GPCP estimates in the northeastern US. These results suggest that Q2 may have difficulty accurately estimating heavy rain and snow events, while GPCP may have an inability to capture some intense precipitation events, which warrants further investigation. In part II of this study, precipitation estimates from five reanalyses (ERA-Interim, MERRA2, JRA-55, CFSR, and 20CR) are compared against the GPCP satellite-gauge (SG) combined product over the CONUS during the period from 1980 through 2013. Compared to the annual averaged precipitation of 2.38 mm/day from GPCP, CFSR has the same annual mean, ERA-Interim and MERRA2 have negative biases of -9.2% and -3.8% respectively, while JRA-55 and 20CR have positive biases of 9.7% and 12.6% respectively. The reanalyses capture the variability of precipitation distribution over the CONUS as derived from GPCP; however, large regional differences exist. The reanalyses generally overestimate the precipitation over the western part of the country throughout the year, which could be due to the difficulty of accurately estimating precipitation over complex terrain. Underestimations in reanalyses over the northeastern US during fall and winter seasons indicate that the five selected reanalyses may be less skillful in reproducing snowfall events. Furthermore, systematic errors exist in all five reanalysese suggest that their physical processes in modeling precipitation need to be improved in the future. We also conduct a long-term trend analysis of precipitation over the CONUS using GPCP and reanalyzed precipitation products from 1980 to 2013. Based on the linear regression of GPCP data, there is a decreasing trend of 2.00 mm/year. For spatial distribution, only north-central and northeastern parts of the county show positive trends, while other areas show negative trends on through the course of a year. Compared to the GPCP observed long-term trend of precipitation, all reanalyses except for 20CR exhibit similar inter-annual variation. Although comprehensive reanalyses that assimilate both satellite and in-situ observations can provide more reasonable precipitation estimates, substantial efforts are still required to further improve the reanalyzed precipitation over the CONUS

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

Improving Satellite Quantitative Precipitation Estimates By Incorporating Deep Convective Cloud Optical Depth

As Deep Convective Systems (DCSs) are responsible for most severe weather events, increased understanding of these systems along with more accurate satellite precipitation estimates will improve NWS (National Weather Service) warnings and monitoring of hazardous weather conditions. A DCS can be classified into convective core (CC) regions (heavy rain), stratiform (SR) regions (moderate-light rain), and anvil (AC) regions (no rain). These regions share similar infrared (IR) brightness temperatures (BT), which can create large errors for many existing rain detection algorithms. This study assesses the performance of the National Mosaic and Multi-sensor Quantitative Precipitation Estimation System (NMQ) Q2, and a simplified version of the GOES-R Rainfall Rate algorithm (also known as the Self-Calibrating Multivariate Precipitation Retrieval, or SCaMPR), over the state of Oklahoma (OK) using OK MESONET observations as ground truth. While the average annual Q2 precipitation estimates were about 35% higher than MESONET observations , there were very strong correlations between these two data sets for multiple temporal and spatial scales. Additionally, the Q2 estimated precipitation distributions over the CC, SR, and AC regions of DCSs strongly resembled the MESONET observed ones, indicating that Q2 can accurately capture the precipitation characteristics of DCSs although it has a wet bias . SCaMPR retrievals were typically three to four times higher than the collocated MESONET observations, with relatively weak correlations during a year of comparisons in 2012. Overestimates from SCaMPR retrievals that produced a high false alarm rate were primarily caused by precipitation retrievals from the anvil regions of DCSs when collocated MESONET stations recorded no precipitation. A modified SCaMPR retrieval algorithm, employing both cloud optical depth and IR temperature, has the potential to make significant improvements to reduce the SCaMPR false alarm rate of retrieved precipitation especially over non-precipitating (anvil) regions of a DCS. Preliminary testing of this new algorithm to identify precipitating area has produced significant improvements over the current SCaMPR algorithm. This modified version of SCaMPR can be used to provide precipitation estimates in gaps of radar and rain gauge coverage to aid in hydrological and flood forecasting

Estimation of Rain Intensity Spectra over the Continental US Using Ground Radar-Gauge Measurements

A high-resolution surface rainfall product is used to estimate rain characteristics over the continental US as a function of rain intensity. By defining each data at 4-km horizontal resolutions and 1-h temporal resolutions as an individual precipitating/nonprecipitating sample, statistics of rain occurrence and rain volume including their geographical and seasonal variations are documented. Quantitative estimations are also conducted to evaluate the impact of missing light rain events due to satellite sensors' detection capabilities. It is found that statistics of rain characteristics have large seasonal and geographical variations across the continental US. Although heavy rain events (> 10 mm/hr.) only occupy 2.6% of total rain occurrence, they may contribute to 27% of total rain volume. Light rain events (< 1.0 mm/hr.), occurring much more frequently (65%) than heavy rain events, can also make important contributions (15%) to the total rain volume. For minimum detectable rain rates setting at 0.5 and 0.2 mm/hr which are close to sensitivities of the current and future space-borne precipitation radars, there are about 43% and 11% of total rain occurrence below these thresholds, and they respectively represent 7% and 0.8% of total rain volume. For passive microwave sensors with their rain pixel sizes ranging from 14 to 16 km and the minimum detectable rain rates around 1 mm/hr., the missed light rain events may account for 70% of train occurrence and 16% of rain volume. Statistics of rain characteristics are also examined on domains with different temporal and spatial resolutions. Current issues in estimates of rain characteristics from satellite measurements and model outputs are discussed

Comprehensive evaluation of high-resolution satellite-based precipitation products over China

Characterizing the errors in satellite-based precipitation estimation products is crucial for understanding their effects in hydrological applications. Six precipitation products derived from three algorithms are comprehensively evaluated against gauge data over mainland China from December 2006 to November 2010. These products include three satellite-only estimates: the Global Satellite Mapping of Precipitation Microwave-IR Combined Product (GSMaP_MVK), the Climate Prediction Center (CPC) MORPHing (CMORPH), and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), as well as their gauge-corrected counterparts: the GSMaP Gauge-calibrated Product (GSMaP_Gauge), bias-corrected CMORPH (CMORPH_CRT), and PERSIANN Climate Data Record (PERSIANN-CDR). Overall, the bias-correction procedures largely reduce various errors for the three groups of satellite-based precipitation products. GSMaP_Gauge produces better fractional coverage with the highest correlation (0.95) and the lowest RMSE (0.53 mm/day) but also high RB (15.77%). In general, CMORPH_CRT amounts are closer to the gauge reference. CMORPH shows better performance than GSMaP_MVK and PERSIANN with the highest CC (0.82) and the lowest RMSE (0.93 mm/day), but also presents a relatively high RB (-19.60%). In winter, all six satellite precipitation estimates have comparatively poor capability, with the IR-based PERSIANN_CDR exhibiting the closest performance to the gauge reference. Both satellite-only and gauge-corrected satellite products show poor capability in detecting occurrence of precipitation with a low POD (40%)