Satellite Quantitative Precipitation Estimates Research Articles

NPreciSe - An Automated Satellite Precipitation Product Assessment Tool.

Satellite-based Quantitative Precipitation Estimates (QPE) are indirect estimates of precipitation rates and as such are often prone to errors, warranting a need for characterizing the associated uncertainties before being used in application-specific studies. Moreover, multiple satellite-based QPE products are offered through different agencies, each with their own specifications, formats and requirements, posing a challenge to understanding the products uncertainties. This manuscript presents a standardized validation system named NPreciSe - NOAA Satellite-based Precipitation Validation System, which assesses the performance of satellite-based precipitation products in near real-time over the continental United States. NPreciSe is coupled with a user-interactive web platform and built using an open-source software, Python. It is structured to help (1) the end-users determine the best satellite QPE for their specific application, and (2) the algorithm developers identify systematic biases in QPE retrievals. This manuscript presents the capabilities of the NPreciSe, discusses the methodology adopted in developing the standardized validation system, and introduces the web portal.

Improving Multisensor Precipitation Estimation via Adaptive Conditional Bias–Penalized Merging of Rain Gauge Data and Remotely Sensed Quantitative Precipitation Estimates

Abstract We describe and evaluate adaptive conditional bias–penalized cokriging (CBPCK) for improved multisensor precipitation estimation using rain gauge data and remotely sensed quantitative precipitation estimates (QPE). The remotely sensed QPEs used are radar-only and radar–satellite-fused estimates. For comparative evaluation, true validation is carried out over the continental United States (CONUS) for 13–30 September 2015 and 7–9 October 2016. The hourly gauge data, radar-only QPE, and satellite QPE used are from the Hydrometeorological Automated Data System, Multi-Radar Multi-Sensor System, and Self-Calibrating Multivariate Precipitation Retrieval (SCaMPR), respectively. For radar–satellite fusion, conditional bias–penalized Fisher estimation is used. The reference merging technique compared is ordinary cokriging (OCK) used in the National Weather Service Multisensor Precipitation Estimator. It is shown that, beyond the reduction due to mean field bias (MFB) correction, both OCK and adaptive CBPCK additionally reduce the unconditional root-mean-square error (RMSE) of radar-only QPE by 9%–16% over the CONUS for the two periods, and that adaptive CBPCK is superior to OCK for estimation of hourly amounts exceeding 1 mm. When fused with the MFB-corrected radar QPE, the MFB-corrected SCaMPR QPE for September 2015 reduces the unconditional RMSE of the MFB-corrected radar by 4% and 6% over the entire and western half of the CONUS, respectively, but is inferior to the MFB-corrected radar for estimation of hourly amounts exceeding 7 mm. Adaptive CBPCK should hence be favored over OCK for estimation of significant amounts of precipitation despite larger computational cost, and the SCaMPR QPE should be used selectively in multisensor QPE.

Accuracy of CHIRPS Satellite-Rainfall Products over Mainland China

Precipitation is the main component of global water cycle. At present, satellite quantitative precipitation estimates (QPEs) are widely applied in the scientific community. However, the evaluations of satellite QPEs have some limitations in terms of the deficiency in observation, evaluation methodology, the selection of time windows for evaluation and short periods for evaluation. The objective of this work is to make some improvements by evaluating the spatio-temporal pattern of the long-terms Climate Hazard Group InfraRed Precipitation Satellite’s (CHIRPS’s) QPEs over mainland China. In this study, we compared the daily precipitation estimates from CHIRPS with 2480 rain gauges across China and gridded observation using several statistical metrics in the long-term period of 1981–2014. The results show that there is significant difference between point evaluation and grid evaluation for CHIRPS. CHIRPS has better performance for a large amount of precipitation than it does for arid and semi-arid land. The change in good performance zones has strong relationship with monsoon’s movement. Therefore, CHIRPS performs better in river basins of southern China and exhibits poor performance in river basins in northwestern and northern China. Moreover, CHIRPS exhibits better in warm season than in Winter, owing to its limited ability to detect snowfall. Nevertheless, CHIRPS is moderately sensitive to the precipitation from typhoon weather systems. The limitations for CHIRPS result from the Tropical Rainfall Measuring Mission (TRMM) 3B42 estimates’ accuracy and valid spatial coverage.

Incorporating Satellite Precipitation Estimates into a Radar-Gauge Multi-Sensor Precipitation Estimation Algorithm

This paper presents a new and enhanced fusion module for the Multi-Sensor Precipitation Estimator (MPE) that would objectively blend real-time satellite quantitative precipitation estimates (SQPE) with radar and gauge estimates. This module consists of a preprocessor that mitigates systematic bias in SQPE, and a two-way blending routine that statistically fuses adjusted SQPE with radar estimates. The preprocessor not only corrects systematic bias in SQPE, but also improves the spatial distribution of precipitation based on SQPE and makes it closely resemble that of radar-based observations. It uses a more sophisticated radar-satellite merging technique to blend preprocessed datasets, and provides a better overall QPE product. The performance of the new satellite-radar-gauge blending module is assessed using independent rain gauge data over a five-year period between 2003–2007, and the assessment evaluates the accuracy of newly developed satellite-radar-gauge (SRG) blended products versus that of radar-gauge products (which represents MPE algorithm currently used in the NWS (National Weather Service) operations) over two regions: (I) Inside radar effective coverage and (II) immediately outside radar coverage. The outcomes of the evaluation indicate (a) ingest of SQPE over areas within effective radar coverage improve the quality of QPE by mitigating the errors in radar estimates in region I; and (b) blending of radar, gauge, and satellite estimates over region II leads to reduction of errors relative to bias-corrected SQPE. In addition, the new module alleviates the discontinuities along the boundaries of radar effective coverage otherwise seen when SQPE is used directly to fill the areas outside of effective radar coverage.

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.

Utilizing satellite precipitation estimates for streamflow forecasting via adjustment of mean field bias in precipitation data and assimilation of streamflow observations

Summary This study explores mitigating bias in satellite quantitative precipitation estimates (SQPE) and improving hydrologic predictions at ungauged locations via adjustment of the mean field bias (MFB) in SQPE and data assimilation (DA) of streamflow observations in a distributed hydrologic model. In this study, a variational procedure is used to adjust MFB in Climate Prediction Center MORPHing (CMORPH) SQPE and assimilate streamflow observations at the outlet of Elk River Basin in Missouri into the distributed Sacramento Soil Moisture Accounting (SAC-SMA) and kinematic wave routing models. The benefits of assimilation are assessed by comparing the streamflow predictions with or without DA at both the outlet and an upstream location, and by comparing the soil moisture grids forced by CMORPH SQPE against those forced by higher-quality multisensor quantitative precipitation estimates (MQPE) from National Weather Service. Special attention is given to the dependence of the efficacy of DA on the quality and latency of the SQPE, and the impact of dynamic correction of MFB in the SQPE via DA. The results show that adjusting MFB in CMORPH SQPE in addition to assimilating outlet flow reduces 66% of the bias in the CMORPH SQPE analysis and the RMSE of 12-h streamflow predictions by 81% at the outlet and 34–62% at interior locations of the catchment. Compared to applying a temporally invariant MFB for the entire storm, the DA-based, dynamic MFB correction reduces the RMSE of 6-h streamflow prediction by 63% at the outlet and 39–69% at interior locations. It is also shown that the accuracy of streamflow prediction deteriorates if the delineation of the precipitation area by CMORPH SQPE is significantly different, as measured by the Hausdorff distance, from that by MQPE. When compared with adjusting MFB in the CMORPH SQPE over the entire assimilation window, adjusting the MFB for all but the latest 18 h (i.e., the latency of CMORPH SQPE) within the assimilation window reduces the mean square error (MSE)-based skill score of streamflow predictions at the outlet by up to 0.08 and at interior locations by up to 0.13.

Utility of SCaMPR Satellite versus Ground-Based Quantitative Precipitation Estimates in Operational Flood Forecasting: The Effects of TRMM Data Ingest

Abstract This study examines the utility of satellite-based quantitative precipitation estimates (QPEs) from the Self-Calibrating Multivariate Precipitation Retrieval (SCaMPR) algorithm for hydrologic prediction. In this work, two sets of SCaMPR QPEs, one without and the other with Tropical Rainfall Measurement Mission (TRMM) version 6 data integrated, were used as input forcing to the lumped National Weather Service hydrologic model to retrospectively generate flow simulations for 10 Texas catchments over 2000–07. The year 2000 was used for the model spinup, 2001–04 for calibration, and 2005–07 for validation. The results were validated using observed streamflow alongside similar simulations obtained using interpolated gauge QPEs with varying gauge network densities, and still others using the operational radar–gauge multisensor product (MAPX). The focus of the evaluation was on the high-flow events. A number of factors that could impact the relative utility of SCaMPR satellite QPE and gauge-only analysis (GMOSAIC) for flood prediction were examined, namely, 1) the incremental impacts of TRMM version 6 data ingest, 2) gauge density, 3) effects of calibration approaches, and 4) basin properties. Results indicate that ground-sensor-based QPEs in a broad sense outperform SCaMPR QPEs, while SCaMPR QPEs are competitive in a minority of catchments. TRMM ingest helped substantially improve the SCaMPR QPE–based simulation results. Change in calibration forcing, that is, calibrating the model using individual QPEs rather than the MAPX (the most accurate QPE), yielded overall improvements to the simulation accuracy but did not change the relative performance of the QPEs.

Joint distribution of multiplicative errors in radar and satellite QPEs and its use in estimating the conditional exceedance probability

This paper characterizes the joint distribution of multiplicative errors (ME) in radar (R) and satellite (S) quantitative precipitation estimates (QPEs). A semi-parametric framework is established on the basis of this joint distribution to describe the probability of rainfall exceeding a particular threshold given concurrent R and S-based estimates (referred to as conditional exceedance probability, or CEP). This framework entails integrating copula-based joint distributions of MEs over a range of rainfall amounts to yield the joint probability of exceedance, which forms the basis for estimating CEP. In demonstrating this approach, MEs were computed for R (Weather Surveillance Radar-1988 Doppler) and S (Self-calibrating Multivariate Precipitation Retrieval) for central Texas over 2000–2007 using gauge records as the reference. Analysis of the MEs in R and S reveals a substantial correlation between the two, and it also shows that the interdependence is complex as a considerable portion of S QPEs are negatively biased while their concurrent R values are bias-neutral. CEP values from the semi-parametric approach is found to be generally superior to those empirically derived based on rainfall estimates: it yields values for a wide range of rainfall thresholds and suffers much fewer discontinuities and artifacts that the empirical results exhibit. For the lower range of S and R thresholds where sample size is relatively large (i.e., <20mmh−1 for the summer), the two sets of CEPs bear close resemblance, with both showing a relatively weak, but nevertheless substantial dependence on the threshold value for S. These findings confirm the plausibility of the semi-parametric CEP values, and demonstrate the utility of S QPEs in improving the confidence in rainfall exceedance under this framework.

Comparative Strengths of SCaMPR Satellite QPEs with and without TRMM Ingest versus Gridded Gauge-Only Analyses

Abstract This paper assesses the accuracy of satellite quantitative precipitation estimates (QPEs) from two versions of the Self-Calibrating Multivariate Precipitation Retrieval (SCaMPR) algorithm relative to that of gridded gauge-only QPEs. The second version of SCaMPR uses the QPEs from Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar and Microwave Imager as predictands whereas the first version does not. The assessments were conducted for 22 catchments in Texas and Louisiana against National Weather Service operational multisensor QPE. Particular attention was given to the density below which SCaMPR QPEs outperform gauge-only QPEs and effects of TRMM ingest. Analyses indicate that SCaMPR QPEs can be competitive in terms of correlation and CSI against sparse gauge networks (with less than one gauge per 3200–12 000 km2) and over 1–3-h scale, but their relative strengths diminish with temporal aggregation. In addition, the major advantage of SCaMPR QPEs is its relatively low false alarm rates, whereas gauge-only QPEs exhibit better skill in detecting rainfall—though the detection skill of SCaMPR QPEs tends to improve at higher rainfall thresholds. Moreover, it was found that ingesting TRMM QPEs help mitigate the positive overall bias in SCaMPR QPEs, and improve the detection of moderate–heavy and particularly wintertime precipitation. Yet, it also tends to elevate the false alarm rate, and its impacts on detection rates can be slightly negative for summertime storms. The implications for adoption of TRMM and Global Precipitation Measurement (GPM) QPEs for NWS operations are discussed.

Utility of Quantitative Precipitation Estimates for high resolution hydrologic forecasts in mountain watersheds of the Colorado Front Range

Quantitative Precipitation Estimates (QPEs) can serve as input to distributed hydrologic models to issue flood and flash flood forecasts in mountain watersheds. Improvements in flood predictions are expected as the quality of radar, multisensor and satellite observations improves. In this study, we assess the value of ten different high resolution QPEs (hourly, 4-km) in four study basins of the Colorado Front Range using a calibrated distributed hydrologic model as a verification tool. To evaluate QPE skill, we compared the precipitation properties at the site (i.e., rain gauge location), basin-average and regional scales and evaluated their influence on the simulated basin response, including the outlet discharge, runoff mechanisms and seasonal water balance. We also analyzed the value of gridded QPEs with respect to uniform forcing derived from rain gauges. Results reveal that radar and multisensor QPEs lead to improved hydrologic model performance compared to simulations driven with rain gauge data only with respect to the observed streamflow. Satellite QPEs exhibit lower overall streamflow simulation skill compared with estimates derived from radar-based QPEs, but are preferable to assuming uniform forcing from nearby rain gauges in the mountain settings studied here. However, satellite QPEs preserve the fundamental properties of the basin response, including a simple scaling relation between the relative spatial variability of runoff and its magnitude. As a result, satellite QPE products open new avenues for forecasting in regions with limited access and sparse observations.