scholarly journals Hydrometeorological Assessment of Satellite and Model Precipitation Products over Taiwan

2021 ◽  
Author(s):  
Pin-Lun Li ◽  
Chia-Jeng Chen ◽  
Liao-Fan Lin
Keyword(s):  
Extreme Event ◽  
Wrf Model ◽  
Warm Season ◽  
Flow Simulation ◽  
Cold Season ◽  
Regional Variations ◽  
Precipitation Measurement ◽  
Precipitation Estimation ◽  
Global Precipitation ◽  
Model Precipitation

AbstractSatellite and model precipitation such as the Global Precipitation Measurement (GPM) data are valuable in hydrometeorological applications. This study investigates the performance of various satellite and model precipitation products in Taiwan from 2015 to 2017, including data derived from the Integrated Multi-satellitE Retrievals for GPM Early and Final Runs (IMERG_E and IMERG_F), Global Satellite Mapping of Precipitation_near-real-time (GSMaP_NRT), and the Weather Research and Forecasting (WRF) model. We assess these products by comparing them against data collected from 304 surface stations and gauge-based gridded data. Our assessment emphasizes factors influential in precipitation estimation, such as season, temperature, elevation, and extreme event. Further, we assess the hydrological response to each precipitation product via continuous flow simulation in two selected watersheds. The results indicate that the performance of these precipitation products is subject to seasonal and regional variations. The satellite products (i.e., IMERG and GSMaP) perform better than the model (i.e., WRF) in the warm season and vice versa in the cold season, most apparently in northern Taiwan. For selected extreme events, WRF can simulate better rainfall amount and distribution. The seasonal and regional variations in precipitation estimation are also reflected in flow simulation: IMERG in general produces the most rational flow simulation, GSMaP tends to overestimate and be least useful for hydrological applications, while WRF simulates high flows that show accurate time to the peak flows and are better in the southern watershed.

scholarly journals Structure and Evolution of a Warm Frontal Precipitation Band during the GPM Cold Season Precipitation Experiment (GCPEx)

2017 ◽  
Vol 145 (2) ◽  
pp. 473-493 ◽  
Author(s):  
Brian A. Colle ◽  
Aaron R. Naeger ◽  
Andrew Molthan
Keyword(s):  
Wrf Model ◽  
Cold Season ◽  
Early Maturity ◽  
Low Level ◽  
The North ◽  
Warm Front ◽  
Precipitation Measurement ◽  
Precipitation Experiment ◽  
Precipitation Band ◽  
Global Precipitation

This paper describes the evolution of an intense precipitation band associated with a relatively weak warm front observed during the Global Precipitation Measurement (GPM) Mission Cold Season Precipitation Experiment (GCPEx) over southern Ontario, Canada, on 18 February 2012. The warm frontal precipitation band went through genesis, maturity, and decay over a 5–6-h period. The Weather Research and Forecasting (WRF) Model nested down to 1-km grid spacing was able to realistically predict the precipitation band evolution, albeit somewhat weaker and slightly farther south than observed. Band genesis began in an area of precipitation with embedded convection to the north of the warm front in a region of weak frontogenetical forcing at low levels and a weakly positive to slightly negative moist potential vorticity (MPV*) from 900 to 650 hPa. A midlevel dry intrusion helped reduce the midlevel stability, while the precipitation band intensified as the low-level frontogenesis intensified in a sloping layer with the warm front. Aggregates of unrimed snow occurred within the band during early maturity, while more supercooled water and graupel occurred as the upward motion increased because of the frontogenetical circulation. As the low-level cyclone moved east, the low-level deformation decreased and the column stabilized for vertical and slantwise ascent, and the warm frontal band weakened. A WRF experiment turning off latent heating resulted in limited precipitation band development and a weaker warm front, while turning off latent cooling only intensified the frontal precipitation band as additional midlevel instability compensated for the small decrease in frontogenetical forcing.

scholarly journals Evaluation of Cloud Microphysical Schemes for a Warm Frontal Snowband during the GPM Cold Season Precipitation Experiment (GCPEx)

2017 ◽  
Vol 145 (11) ◽  
pp. 4627-4650 ◽  
Author(s):  
Aaron R. Naeger ◽  
Brian A. Colle ◽  
Andrew Molthan
Keyword(s):  
Wrf Model ◽  
Cold Season ◽  
Cloud Water ◽  
Explicit Calculation ◽  
Particle Properties ◽  
Water Condensation ◽  
Precipitation Measurement ◽  
Precipitation Experiment ◽  
Cloud Ice ◽  
Global Precipitation

Detailed observations from the Global Precipitation Measurement (GPM) mission Cold Season Precipitation Experiment (GCPEx) of an intense warm frontal band on 18 February 2012 were used to evaluate several bulk microphysical parameterizations within the NASA-Unified Weather Research and Forecasting (NU-WRF) Model. These included the Predicted Particle Properties (P3), Morrison (MORR), Stony Brook University (SBU), and Goddard four-class ice (4ICE) microphysics schemes. All schemes were able to predict the snowband, but the simulated intensities varied because of various assumptions in these schemes. The saturation adjustment scheme within MORR promoted excessive amounts of cloud water evaporational cooling in the warm sector, which contributed to a decrease in midlevel instability approaching the frontal band and thus a weaker band. In contrast, the explicit calculation of cloud water condensation/evaporation in the P3 scheme produced limited amounts of evaporational cooling, which allowed for greater midlevel instability to support band development. The P3 and SBU schemes produced moderate rime/graupel mass within the band that was confirmed by observations, while the MORR and 4ICE schemes drastically underpredicted the graupel mass. The high-density, fast-falling rimed particles in P3 underwent weak sublimation and melting, which helped promote a stronger horizontal temperature gradient and greater low-level instability along the frontal band compared to the other schemes. Overall, the schemes that use specified thresholds for converting between the predefined ice-phase categories of cloud ice, snow, and graupel had the most unrepresentative hydrometeor types. These results highlight the advantage of predicting ice particle properties and explicitly calculating cloud water condensation/evaporation in the P3 scheme.

Incorporating NASA Spaceborne Radar Data into NOAA National Mosaic QPE System for Improved Precipitation Measurement: A Physically Based VPR Identification and Enhancement Method

2013 ◽  
Vol 14 (4) ◽  
pp. 1293-1307 ◽  
Author(s):  
Yixin Wen ◽  
Qing Cao ◽  
Pierre-Emmanuel Kirstetter ◽  
Yang Hong ◽  
Jonathan J. Gourley ◽  
...  
Keyword(s):  
Tropical Rainfall Measuring Mission ◽  
Radar Data ◽  
Cold Season ◽  
Dual Frequency ◽  
Surface Precipitation ◽  
Precipitation Measurement ◽  
Trmm Pr ◽  
Physically Based ◽  
Precipitation Estimation ◽  
Enhancement Method

Abstract This study proposes an approach that identifies and corrects for the vertical profile of reflectivity (VPR) by using Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) measurements in the region of Arizona and southern California, where the ground-based Next Generation Weather Radar (NEXRAD) finds difficulties in making reliable estimations of surface precipitation amounts because of 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 is employed to obtain a representative VPR at S band from the TRMM PR measurement at Ku band. Then the representative VPR is convolved with ground radar beam sampling properties to compute apparent VPRs for enhancing NEXRAD quantitative precipitation estimation (QPE). The VPR-IE methodology is evaluated with several stratiform precipitation events during the cold season and is compared to two other statistically based correction methods, that is, the TRMM PR–based rainfall calibration and a range ring–based adjustment scheme. The results show that the VPR-IE has the best overall performance and provides much more accurate surface rainfall estimates than the original ground-based radar QPE. The potential of the VPR-IE method could be further exploited and better utilized when the Global Precipitation Measurement Mission's dual-frequency PR is launched in 2014, with anticipated accuracy improvements and expanded latitude coverage.

Improving Precipitation Retrieval by Brightness Temperature Temporal Variation (ΔTB): Definition, Computation, and Application

2021 ◽  
Author(s):  
Yalei You ◽  
Christa Peters-Lidard ◽  
Stephen Munchak ◽  
Sarah Ringerud
Keyword(s):  
Temporal Variation ◽  
Brightness Temperature ◽  
Land Surface ◽  
Microwave Radiometer ◽  
Daily Rainfall ◽  
Surface Emissivity ◽  
Time Dimension ◽  
Precipitation Measurement ◽  
Precipitation Estimation ◽  
Global Precipitation

<p>Current microwave precipitation retrieval algorithms utilize the instantaneous brightness temperature (TB) from a single satellite to estimate the precipitation rate. This study proposed to add the time-dimension into the precipitation estimation process by using the TB (or emissivity) temporal variation (ΔTB or Δe) derived from the Global Precipitation Measurement (GPM) microwave radiometer constellation.  Results showed that (1) ΔTB can improve the precipitation estimation over the cold surfaces (i.e., snow-covered region) through minimizing the microwave land surface emissivity’s influence; (2) Δe under the clear-sky conditions can accurately estimate the daily rainfall accumulation; and (3) ΔTB can be used to identify the liquid raindrop signature over the low surface emissivity areas. This study highlights the importance of maintaining the current passive microwave satellite constellation.</p>

scholarly journals Comparison of the GPCP 1DD Precipitation Product and NEXRAD Q2 Precipitation Estimates over the Continental United States

2016 ◽  
Vol 17 (6) ◽  
pp. 1837-1853 ◽  
Author(s):  
Wenjun Cui ◽  
Xiquan Dong ◽  
Baike Xi ◽  
Ronald Stenz
Keyword(s):  
United States ◽  
Warm Season ◽  
Correlation Coefficients ◽  
Heavy Rain ◽  
Global Precipitation Climatology Project ◽  
Cold Season ◽  
Precipitation Estimation ◽  
Precipitation Estimates ◽  
Annual Means ◽  
Mean Differences

Abstract This study compares the Global Precipitation Climatology Project (GPCP) 1 Degree Daily (1DD) precipitation estimates over the continental United States (CONUS) with National Mosaic and Multi-Sensor Quantitative Precipitation Estimation (NMQ) Next Generation (Q2) estimates. Spatial averages of monthly and yearly accumulated precipitation were computed based on daily estimates from six selected regions during the period 2010–12. Both Q2 and GPCP daily precipitation estimates show that precipitation amounts over southern regions (<40°N) are generally larger than northern regions (≥40°N). Correlation coefficients for daily estimates over 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. For spatially averaged precipitation values averaged from grid boxes within selected regions, GPCP and Q2 estimates are well correlated, especially for monthly accumulated precipitation, with strong correlations ranging from 0.903 to 0.954. 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 for Q2 vs 941.79 mm for GPCP), Q2 estimates are much larger than GPCP over the central and southern United States and less than GPCP estimates in the northeastern United States. These results suggest that Q2 may have difficulties accurately estimating heavy rain and snow events, while GPCP may have an inability to capture some intense precipitation events, which warrants further investigation.

scholarly journals The Next-Generation Goddard Convective–Stratiform Heating Algorithm: New Tropical and Warm-Season Retrievals for GPM

2018 ◽  
Vol 31 (15) ◽  
pp. 5997-6026 ◽  
Author(s):  
Stephen E. Lang ◽  
Wei-Kuo Tao
Keyword(s):  
Warm Season ◽  
Tropical Rainfall Measuring Mission ◽  
Shallow Convection ◽  
Heating Rates ◽  
Low Level ◽  
Precipitation Measurement ◽  
Cloud Resolving Model ◽  
Upper Level ◽  
Heating Profiles ◽  
Global Precipitation

The Goddard convective–stratiform heating (CSH) algorithm, used to estimate cloud heating in support of the Tropical Rainfall Measuring Mission (TRMM), is upgraded in support of the Global Precipitation Measurement (GPM) mission. The algorithm’s lookup tables (LUTs) are revised using new and additional cloud-resolving model (CRM) simulations from the Goddard Cumulus Ensemble (GCE) model, producing smoother heating patterns that span a wider range of intensities because of the increased sampling and finer GPM product grid. Low-level stratiform cooling rates are reduced in the land LUTs for a given rain intensity because of the rain evaporation correction in the new four-class ice (4ICE) scheme. Additional criteria, namely, echo-top heights and low-level reflectivity gradients, are tested for the selection of heating profiles. Those resulting LUTs show greater and more precise variation in their depth of heating as well as a tendency for stronger cooling and heating rates when low-level dB Z values decrease toward the surface. Comparisons versus TRMM for a 3-month period show much more low-level heating in the GPM retrievals because of increased detection of shallow convection, while upper-level heating patterns remain similar. The use of echo tops and low-level reflectivity gradients greatly reduces midlevel heating from ~2 to 5 km in the mean GPM heating profile, resulting in a more top-heavy profile like TRMM versus a more bottom-heavy profile with much more midlevel heating. Integrated latent heating rates are much better balanced versus surface rainfall for the GPM retrievals using the additional selection criteria with an overall bias of +4.3%.

The Global Precipitation Climatology Project Version 3 Products

2021 ◽  
Author(s):  
George J. Huffman ◽  
Ali Behrangi ◽  
Robert F. Adler ◽  
David T. Bolvin ◽  
Eric J. Nelkin ◽  
...  
Keyword(s):  
Global Precipitation Climatology Project ◽  
Climate Data ◽  
Precipitation Climatology ◽  
Global Precipitation Climatology Centre ◽  
Early Results ◽  
Precipitation Measurement ◽  
Key Points ◽  
Precipitation Estimation ◽  
Global Precipitation Measurement ◽  
Global Precipitation

<p>The Global Precipitation Climatology Project (GPCP) is currently providing a next-generation Version 3.1 Monthly product, which covers the period 1983-2019.  This modernized product includes higher spatial resolution (0.5°x0.5°); a wider coverage (60°N-S) by geosynchronous IR estimates, now based on the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR) algorithm, with monthly recalibration using Goddard Profiling (GPROF) algorithm retrievals from selected passive microwave sensors; and improved calibrations of Television-Infrared Operational Satellite (TIROS) Operational Vertical Sounder (TOVS) and Advanced Infrared Sounder (AIRS) precipitation, used outside 60ºN-S.  The merged satellite estimate is adjusted to the Tropical Combined Climatology (TCC) at lower latitudes, and the Merged CloudSat, TRMM, and GPM (MCTG) climatology at higher latitudes.  Finally, V3.1 provides a merger of the satellite-only estimates with the Global Precipitation Climatology Centre (GPCC) monthly 1°x1° gauge analyses. </p><p>As well, the GPCP team is advancing a companion global Version 3 Daily product, in which the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (GPM) mission (IMERG) Final Run V06 estimates are used where available (initially restricted to 60°N-S), and rescaled TOVS/AIRS data in high-latitude areas, all calibrated to the GPCP V3.1 Monthly estimate.  Since IMERG currently extends back to June 2000, daily PERSIANN-CDR data will be used for the period January 1983–May 2000 to complete the record.</p><p>This presentation will provide early results for, and the latest status of, the Monthly and Daily GPCP products as a function of time and region.  Key points include examining homogeneity over time and across time and space boundaries between input datasets.  One key activity is to refine the V3 products while we continue to produce the Version 2 GPCP products for on-going use.</p>

Satellite-Based Precipitation Estimation and Its Application for Streamflow Prediction over Mountainous Western U.S. Basins

2014 ◽  
Vol 53 (12) ◽  
pp. 2823-2842 ◽  
Author(s):  
Ali Behrangi ◽  
Konstantinos Andreadis ◽  
Joshua B. Fisher ◽  
F. Joseph Turk ◽  
Stephanie Granger ◽  
...  
Keyword(s):  
Tropical Rainfall Measuring Mission ◽  
Current Status ◽  
Streamflow Prediction ◽  
Infrared Sensors ◽  
Infiltration Capacity ◽  
Mountainous Regions ◽  
Earth Observing System ◽  
Precipitation Measurement ◽  
Precipitation Estimation ◽  
Global Precipitation

AbstractRecognizing the importance and challenges inherent to the remote sensing of precipitation in mountainous areas, this study investigates the performance of the commonly used satellite-based high-resolution precipitation products (HRPPs) over several basins in the mountainous western United States. Five HRPPs [Tropical Rainfall Measuring Mission 3B42 and 3B42-RT algorithms, the Climate Prediction Center morphing technique (CMORPH), Precipitation Estimation from Remotely Sensed Imagery Using Artificial Neural Networks (PERSIANN), and the PERSIANN Cloud Classification System (PERSIANN-CCS)] are analyzed in the present work using ground gauge, gauge-adjusted radar, and CloudSat precipitation products. Using ground observation of precipitation and streamflow, the skill of HRPPs and the resulting streamflow simulations from the Variable Infiltration Capacity hydrological model are cross-compared. HRPPs often capture major precipitation events but seldom capture the observed magnitude of precipitation over the studied region and period (2003–09). Bias adjustment is found to be effective in enhancing the HRPPs and resulting streamflow simulations. However, if not bias adjusted using gauges, errors are typically large as in the lower-level precipitation inputs to HRPPs. The results using collocated Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) and CloudSat precipitation data show that missing data, often over frozen land, and limitations in retrieving precipitation from systems that lack frozen hydrometeors contribute to the observed microwave-based precipitation errors transferred to HRPPs. Over frozen land, precipitation retrievals from infrared sensors and microwave sounders show some skill in capturing the observed precipitation climatology maps. However, infrared techniques often show poor detection skill, and microwave sounding in dry atmosphere remains challenging. By recognizing the sources of precipitation error and in light of the operation of the Global Precipitation Measurement mission, further opportunity for enhancing the current status of precipitation retrievals and the hydrology of cold and mountainous regions becomes available.

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