Precipitation Processing System Research Articles

Rainfall dynamics in an ecologically vulnerable area using applied algebraic topology methods

Applied algebraic topology is employed in this work to shed new light on the rainfall dynamics in the Pernambuco state, Brazil. Historical data from the NASA’s Tropical Rainfall Measuring Mission (TRMM) precipitation processing system, for the period 1998 to 2020 with a spatial resolution of 0.25° and temporal resolution of 3 h is used to construct correlation matrices in different time frames. Matrices are then analyzed in terms of topological constructs of network theory to yield novel insights into this highly complex phenomenon in this semiarid, ecologically vulnerable area. The outcomes of the algebraic topological analysis reveal clustering patterns of areas and are related to natural climate phenomena. Together with the generality of the applied methodology, the results suggest a broad scope of future applications for the extraction of patterns in datasets related to the changes in the climate system.

Improving S-Band Polarimetric Radar Monsoon Rainfall Estimation with Two-Dimensional Video Disdrometer Observations in South China

The capability to estimate monsoon rainfall is investigated by using S-band polarimetric radar (S-POL) and two-dimensional Video Disdrometer (2DVD) during 2017–2018 in South China. Based on 2 years of 2DVD raindrop size distribution (DSD) observations of monsoon precipitation systems, four different quantitative precipitation estimation (QPE) algorithms were obtained, including R(ZH), R(ZH, ZDR), R(KDP), and R(KDP, ZDR). In order to clearly demarcate the optimal ranges of the four QPE algorithms by considering the impact of the monsoon precipitation system of South China, the optimal ranges of the four QPE algorithms were integrated together according to the characteristics of different QPE algorithms in the reflectivity-differential reflectivity (ZH-ZDR) space distribution by reference to 8 monsoon rainfall events from 2016 to 2020 observed in Guangzhou and Yangjiang S-POL. Then, an optimal algorithm was proposed for the quantitative estimation of monsoon precipitation in South China (2DVD-SCM) using S-POL. The 2DVD-SCM was tested by comparing it with a traditional radar QPE algorithm PPS (WSR-88D Precipitation Processing System); a classical QPE algorithm CSU-HIDRO (Colorado State University-Hydrometeor Identification Rainfall Optimization) for the polarimetric radar; a piecewise fitting algorithm LPA-PFM (Piecewise Fitting Method) based on laser raindrop spectrum. The rainfall event one-by-one test results show that the 2DVD-SCM algorithm performs obviously better than the other three algorithms in most of the rainfall events. The hourly accumulated rainfalls estimated by the 2DVD-SCM algorithm are agreed well with rain gauge observations. The normalized errors (NE) and the root mean square errors (RMSE) values of 2DVD-SCM are remarkably less than the other three algorithms, and the correlation coefficient (CC) values are higher. The results of the classified rain rate test show that the NE and RMSE values of the 2DVD-SCM algorithm are the lowest in all classified rain rates. The overall evaluation results show that the 2DVD-SCM algorithm performs obviously better than the existing three algorithms and have the potential to apply in S-band polarimetric radar monsoon rainfall estimation operational system in South China.

TRMM MICROWAVE IMAGER (TMI) ALIGNMENT AND ALONG-SCAN BIAS CORRECTIONS.

The Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) dataset released by the Precipitation Processing System (PPS) has been updated to a final version following the decommissioning of the TRMM satellite in April 2015. The updates are based on increased knowledge of radiometer calibration and sensor performance issues. In particular, the Global Precipitation Measurement (GPM) Microwave Imager (GMI) is used as a model for many of the TMI updates. This paper discusses two aspects of the TMI data product that have been reanalyzed and updated: alignment and along-scan bias corrections. The TMI pointing accuracy is significantly improved over prior PPS versions, which used at-launch alignment values. ATMI instrument mounting offset is discovered as well as new alignment offsets for the two TMI feedhorns. The original TMI along-scan antenna temperature bias correction is found to be generally accurate over-ocean, but a scene temperature-dependent correction is needed to account for edge-of-scan obstruction. These updates are incorporated into the final TMI data version, improving the quality of the data product and ensuring accurate geophysical parameters can be derived from TMI.

Assessing Radiometric Stability of the 17-Plus-Year TRMM Microwave Imager 1B11 Version-8 (GPM05) Brightness Temperature Product

The NASA Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) has produced a 17-plus-year time-series of calibrated microwave radiances that have remarkable value for investigating the effects of the Earth’s climate change over the tropics. Recently, the Global Precipitation Measurement (GPM) Inter-Satellite Radiometric Calibration (XCAL) Working Group have performed various calibration and corrections that yielded the legacy TMI 1B11 Version 8 (also called GPM05) brightness temperature product, which will be released in late 2017 by the NASA Precipitation Processing System. Since TMI served as the radiometric transfer standard for the TRMM constellation microwave radiometer sensors, it is important to document its accuracy. In this paper, the various improvements applied to TMI 1B11 V8 are summarized, and the radiometric calibration stability is evaluated by comparisons with a radiative transfer model and by XCAL evaluations with the Global Precipitation Measuring Microwave Imager during their 13-month overlap period. Evaluation methods will be described and results will be presented, which demonstrate that TMI has achieved a radiometric stability level of a few deciKelvin over almost two decades.

MRMS QPE Performance East of the Rockies during the 2014 Warm Season

Abstract Multi-Radar Multi-Sensor (MRMS) quantitative precipitation estimation (QPE) radar only (Q3RAD), Q3RAD local gauge corrected (Q3gc), dual polarization (Dual Pol), legacy Precipitation Processing System (PPS), and National Centers for Environmental Prediction (NCEP) stage IV product performance were evaluated for data collected east of the Rockies during the 2014 warm season. For over 22 000 radar QPE–gauge data pairs, Q3RAD had a higher correlation coefficient (0.85) and a lower mean absolute error (9.4 mm) than the Dual Pol (0.83 and 10.5 mm, respectively) and PPS (0.79 and 10.8 mm, respectively). Q3RAD performed best when the radar beam sampled precipitation within or above the melting layer because of its use of a reflectivity mosaic corrected for brightband contamination. Both NCEP stage IV and Q3gc showed improvement over the radar-only QPEs; while stage IV exhibited the lower errors, the performance of Q3gc was remarkable considering the estimates were automatically generated in near–real time. Regional analysis indicated Q3RAD outperformed Dual Pol and PPS over the southern plains, Southeast/mid-Atlantic, and Northeast. Over the northern United States, Q3RAD had a higher wet bias below the melting layer than both Dual Pol and PPS; within and above the melting layer, Q3RAD exhibited the lowest errors. The Q3RAD wet bias was likely due to MRMS’s overestimation of tropical rain areas in continental regions and applying a high yield reflectivity–rain-rate relationship. An adjustment based on precipitation climatology reduced the wet bias errors by ~22% and will be implemented in the operational MRMS in the fall of 2016.

Assessment and Implications of NCEP Stage IV Quantitative Precipitation Estimates for Product Intercomparisons

Abstract The National Centers for Environmental Prediction (NCEP) stage IV quantitative precipitation estimates (QPEs) are used in many studies for intercomparisons including those for satellite QPEs. An overview of the National Weather Service precipitation processing system is provided here so as to set the stage IV product in context and to provide users with some knowledge as to how it is developed. Then, an assessment of the stage IV product over the period 2002–12 is provided. The assessment shows that the stage IV product can be useful for conditional comparisons of moderate-to-heavy rainfall for select seasons and locations. When evaluating the product at the daily scale, there are many discontinuities due to the operational processing at the radar site as well as discontinuities due to the merging of data from different River Forecast Centers (RFCs) that use much different processing algorithms for generating their precipitation estimates. An assessment of the daily precipitation estimates is provided based on the cumulative distribution function for all of the daily estimates for each RFC by season. In addition it is found that the hourly estimates at certain RFCs suffer from lack of manual quality control and caution should be used.

Radar and Multisensor Precipitation Estimation Techniques in National Weather Service Hydrologic Operations

This paper describes techniques used operationally by the National Weather Service (NWS) to prepare gridded multisensor (gauge, radar, and satellite) quantitative precipitation estimates (QPEs) for input into hydrologic forecast models and decision-making systems for river forecasting, flood and flash flood warning, and other hydrologic monitoring purposes. Advanced hydrologic prediction techniques require a spatially continuous representation of the precipitation field, and remote sensor input is critical to achieving this continuity. Although detailed descriptions of individual remote sensor estimation algorithms have been published, this review provides a summary of how the estimates from these various sources are merged into finished products. Emphasis is placed on the Weather Surveillance Radar–1988 Doppler (WSR-88D) Precipitation Processing System (PPS) and the Advanced Weather Interactive Processing System (AWIPS) Multisensor Precipitation Estimator (MPE) algorithms that utilize a combination of in situ rain gauges and remotely sensed measurements to provide a real-time suite of gridded radar and multisensor precipitation products. These two algorithm suites work in series to combine both computer-automated and human-interactive techniques, and they are used routinely at NWS field offices [river forecast centers (RFCs) and weather forecast offices (WFOs)] to support NWS’s broader hydrologic missions. The resulting precipitation products are also available to scientists and engineers outside the NWS; a summary of characteristics and sources of these products is presented.

Adapting an existing visualization application for browser-based deployment: A case study from the Tropical Rainfall Measuring Mission

THOR, the Tool for High-resolution Observation Review, is a data viewer for the Tropical Rainfall Measuring Mission (TRMM) and the upcoming Global Precipitation Measurement (GPM) mission. THOR began as a desktop application, but now it can be accessed with a web browser, making THOR one of the first online tools for visualizing TRMM satellite data (http://pps.gsfc.nasa.gov/thor). In this effort, the reuse of the existing visualization code was maximized and the complexity of new code was minimized by avoiding unnecessary functionality, frameworks, or libraries. The simplicity of this approach makes it potentially attractive to researchers wishing to adapt their visualization applications for online deployment. To enable THOR to run within a web browser, three new pieces of code are written. First, the graphical user interface (GUI) of the desktop application is translated into HTML, JavaScript, and CSS. Second, a simple communication mechanism is developed over HTTP. Third, a virtual GUI is created on the server that interfaces with the image-generating routines of the existing desktop application so that these routines do not need to be modified for online use. While the basic functionality of THOR is now available online, prototyping is ongoing for enhanced 3D imaging and other aspects of both THOR Desktop and THOR Online. Because TRMM data products are complex and periodically reprocessed with improved algorithms, having a tool such as THOR is important to analysts at the Precipitation Processing System where the algorithms are tested and the products generated, stored, and distributed. Researchers also have found THOR useful for taking a first look at individual files before writing their own software to perform specialized calculations and analyses.

Evaluation of Radar Precipitation Estimates from the National Mosaic and Multisensor Quantitative Precipitation Estimation System and the WSR-88D Precipitation Processing System over the Conterminous United States

Abstract This study evaluated 24-, 6-, and 1-h radar precipitation estimated from the National Mosaic and Multisensor Quantitative Precipitation Estimation System (NMQ) and the Weather Surveillance Radar-1988 Doppler (WSR-88D) Precipitation Processing System (PPS) over the conterminous United States (CONUS) for the warm season April–September 2009 and the cool season October 2009–March 2010. Precipitation gauge observations from the Automated Surface Observing System (ASOS) were used as the ground truth. Gridded StageIV multisensor precipitation estimates were applied for supplementary verification. The comparison of the two systems consisted of a series of analyses including the linear correlation coefficient (CC) and the root-mean-square error (RMSE) between the radar precipitation estimates and the gauge observations, large precipitation amount detection categorical scores, and the reliability of precipitation amount distribution. Data stratified for the 12 CONUS River Forecast Centers (RFCs) and for the cold rains events with bright-band effects were analyzed additionally. Major results are 1) the linear CC of NMQ versus ASOS are generally higher than that of PPS versus ASOS over CONUS, while the spatial variations stratified by the RFCs may switch with seasons; 2) compared to the precipitation distribution of ASOS, NMQ shows less deviation than PPS; 3) for the cold rains verified against ASOS, NMQ has higher CC and PPS has lower RMSE for 6-h and higher RMSE for 1-h cold rains; and 4) for the precipitation detection categorical scores, either NMQ or PPS can be superior, depending on the time interval and season. The verification against StageIV gridded precipitation estimates showed that NMQ consistently had higher correlations and lower biases than did PPS.

Product-Error-Driven Uncertainty Model for Probabilistic Quantitative Precipitation Estimation with NEXRAD Data

Abstract Although it is broadly acknowledged that the radar-rainfall (RR) estimates based on the U.S. national network of Weather Surveillance Radar-1988 Doppler (WSR-88D) stations contain a high degree of uncertainty, no methods currently exist to inform users about its quantitative characteristics. The most comprehensive characterization of this uncertainty can be achieved by delivering the products in a probabilistic rather than the traditional deterministic form. The authors are developing a methodology for probabilistic quantitative precipitation estimation (PQPE) based on weather radar data. In this study, they present the central element of this methodology: an empirically based error structure model for the RR products. The authors apply a product-error-driven (PED) approach to obtain a realistic uncertainty model. It is based on the analyses of six years of data from the Oklahoma City, Oklahoma, WSR-88D radar (KTLX) processed with the Precipitation Processing System algorithm of the NEXRAD system. The modeled functional-statistical relationship between RR estimates and corresponding true rainfall consists of two components: a systematic distortion function and a stochastic factor quantifying remaining random errors. The two components are identified using a nonparametric functional estimation apparatus. The true rainfall is approximated with rain gauge data from the Oklahoma Mesonet and the U.S. Department of Agriculture (USDA) Agricultural Research Service Micronet networks. The RR uncertainty model presented here accounts for different time scales, synoptic regimes, and distances from the radar. In addition, this study marks the first time in which results on RR error correlation in space and time are presented.

An Exploratory Multisensor Technique for Quantitative Estimation of Stratiform Rainfall

Abstract Implementation of the National Weather Service Weather Surveillance Radar-1988 Doppler (WSR-88D) radar network provides the potential to monitor rainfall and snowfall accumulations at fine spatial and temporal resolutions. An automated, operational algorithm called the Precipitation Processing System (PPS) uses reflectivity data to estimate precipitation accumulations. The utility of these estimates has yet to be quantified in the Intermountain West during winter months. The accuracy of precipitation estimates from the operational PPS during cool-season, stratiform-precipitation events in Arizona is examined. In addition, a method, with the potential for automation, is developed to improve estimates of precipitation by calibrating infrared data (10.7-μm band) from Geostationary Operational Environmental Satellite-9 using reflectivity-derived rainfall rates from WSR-88D radar. The “multisensor” approach provides more accurate estimates of rainfall across lower elevations during cool-season extratr...

Sensitivity of WSR-88D Rainfall Estimates to the Rain-Rate Threshold and Rain Gauge Adjustment: A Flash Flood Case Study

Abstract A strong thunderstorm produced a flash flood on the evening of 12 July 1996 in Buffalo Creek, Colorado, that caused two deaths and significant property damage. Most of the rain fell in a 1-h time period from 2000 to 2100 MDT. The performance of the WSR-88D rainfall algorithm, Precipitation Processing System, was examined in detail to determine how well it performed. In particular the sensitivity to the algorithm’s rain-rate threshold (hail cap) parameter and the performance of the gauge–radar adjustment subalgorithm on the resulting radar rainfall estimates were examined by comparison with available rain gauge data. It was determined that the WSR-88D rainfall algorithm overestimated the rainfall in general over the radar scanning domain for this event by about 60% relative to the rain gauges although the radar-derived rainfall for the flood-producing storm cell nearly matched the single gauge that sampled it. The derived rainfall over the radar scanning domain was not very sensitive to the settin...

The WSR-88D Rainfall Algorithm

Abstract A detailed description of the operational WSR-88D rainfall estimation algorithm is presented. This algorithm, called the Precipitation Processing System, produces radar-derived rainfall products in real time for forecasters in support of the National Weather Service’s warning and forecast missions. It transforms reflectivity factor measurements into rainfall accumulations and incorporates rain gauge data to improve the radar estimates. The products are used as guidance to issue flood watches and warnings to the public and as input into numerical hydrologic and atmospheric models. The processing steps to quality control and compute the rainfall estimates are described, and the current deficiencies and future plans for improvement are discussed.