Evaluation and Hydrological Validation of GPM Precipitation Products over the Nanliu River Basin, Beibu Gulf

Adequate and high-quality precipitation estimates, from spaceborne precipitation radars, are necessary for a variety of applications in hydrology. In this study, we investigated the performance of two Integrated Multi-satellitE Retrievals for the Global Precipitation Measurement (GPM) mission (IMERG) products, against gauge observations over a small river basin, the Beibu Gulf—the Nanliu River basin, and evaluated their capability of streamflow simulation, based on a conceptual watershed model from April 2014 to December 2016. The results showed that both IMERG_Cal and IMERG_Uncal could roughly capture the spatial patterns of precipitation with slight over/underestimation (Relative Bias (RB) values of 6.5% and −5.5%, respectively) at a basin scale. At grid-cell scales, two IMERG products got an RB of −23.3% to 18.9%, Correlation Coefficient (CC) of 0.521 to 0.744, and Root Mean Square Error (RMSE) of 11.3 to 17.5 mm. There were some considerable errors in heavy precipitation events, and the IMERG significantly overestimated the amounts of these extreme events. The two IMERG products showed a higher accuracy and lower error rate, when detecting the light precipitation. IMERG-driven simulation had a better quality when the model was calibrated with satellite data rather than with rain gauge data. This analysis implied that IMERG products have potential in hydrological applications, in this region, and need further improvement in algorithms.

Download Full-text

Role of stationary convective bands and antecedent conditions on the flood response to the Vaia storm (October 27-30, 2018) in the Eastern Italian Alps

10.5194/egusphere-egu2020-18668 ◽

2020 ◽

Author(s):

Mattia Zaramella ◽

Lorenzo Marchi ◽

Francesco Marra ◽

Francesco Comiti ◽

Stefano Crema ◽

...

Keyword(s):

River Basin ◽

Heavy Precipitation ◽

Rain Gauge ◽

Second Phase ◽

Italian Alps ◽

Spatial Moments ◽

Antecedent Conditions ◽

Basin Scale ◽

Flood Response ◽

The Impact

Between the morning of 27 October 2018 and the evening of 29 October 2018, heavy precipitation over the Eastern Italian Alps led to damaging flooding. The event, which occurred at the end of a climatic anomaly of prolonged drought, developed into two phases, with a first phase (October 27-28) dominated by more stratiform orographically-enhanced precipitation. After a short lull, a second and more intense phase of the event took place on the 29th, when a cold front from the Gulf of Lion entered the Mediterranean basin triggering explosive cyclogenesis. A characteristic of the second phase of the storm is the rainfall organization in well-defined convective bands, some of which persisted at the same location for up to 3 hours. The bands, aligned from southeast to northwest, were initially located downstream of the pre-alpine region.The work aims to investigate the impact of the stationary convective bands and of the dry antecedent conditions on the flood response to the storm. The availability of high-resolution rainfall estimates from weather radar and of dense rain gauge network data, along with flood response observations from stream gauge data and post-event surveys, enables to study the hydrometeorological and hydrological mechanisms associated with this extreme storm and the consequent flood response.Observational and model analyses of the hydrologic runoff in two areas heavily impacted by the storm (Noce river basin, in the Trentino Province, and upper Cordevole river basin, in the Veneto Region) illustrate how the structure and evolution of the stationary convective bands translate into scale dependent flood response. For the upper Cordevole river basin, the event envelope curve shows two peculiar behaviors: (a) basin scale ranging from 1 to 200 km2, with peak unit discharges decreasing from 10 to 4 m3s-1km-2; (b) basin scale ranging from 200 to 2000 km2, with smaller peak unit discharges. The spatial extent of the first region is controlled by the structure of the central convective band. Moreover, the spatial moments of catchment rainfall are exploited to identify the impact of the convective cells motion along the stationary band on the flood response.

Download Full-text

PERSIANN-CCS-CDR, a 3-hourly 0.04° global precipitation climate data record for heavy precipitation studies

Scientific Data ◽

10.1038/s41597-021-00940-9 ◽

2021 ◽

Vol 8 (1) ◽

Author(s):

Mojtaba Sadeghi ◽

Phu Nguyen ◽

Matin Rahnamay Naeini ◽

Kuolin Hsu ◽

Dan Braithwaite ◽

...

Keyword(s):

Spatial Resolution ◽

Heavy Precipitation ◽

Climate Data ◽

Spatiotemporal Resolution ◽

Data Record ◽

Precipitation Estimation ◽

Precipitation Estimates ◽

Global Precipitation ◽

Climate Data Record

AbstractAccurate long-term global precipitation estimates, especially for heavy precipitation rates, at fine spatial and temporal resolutions is vital for a wide variety of climatological studies. Most of the available operational precipitation estimation datasets provide either high spatial resolution with short-term duration estimates or lower spatial resolution with long-term duration estimates. Furthermore, previous research has stressed that most of the available satellite-based precipitation products show poor performance for capturing extreme events at high temporal resolution. Therefore, there is a need for a precipitation product that reliably detects heavy precipitation rates with fine spatiotemporal resolution and a longer period of record. Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System-Climate Data Record (PERSIANN-CCS-CDR) is designed to address these limitations. This dataset provides precipitation estimates at 0.04° spatial and 3-hourly temporal resolutions from 1983 to present over the global domain of 60°S to 60°N. Evaluations of PERSIANN-CCS-CDR and PERSIANN-CDR against gauge and radar observations show the better performance of PERSIANN-CCS-CDR in representing the spatiotemporal resolution, magnitude, and spatial distribution patterns of precipitation, especially for extreme events.

Download Full-text

The Contribution of Rain Gauges in the Calibration of the IMERG Product: Results from the First Validation over Spain

Journal of Hydrometeorology ◽

10.1175/jhm-d-19-0116.1 ◽

2020 ◽

Vol 21 (2) ◽

pp. 161-182 ◽

Cited By ~ 4

Author(s):

Francisco J. Tapiador ◽

Andrés Navarro ◽

Eduardo García-Ortega ◽

Andrés Merino ◽

José Luis Sánchez ◽

...

Keyword(s):

Rain Gauge ◽

Surface Precipitation ◽

Rain Gauges ◽

Precipitation Measurement ◽

Precipitation Estimates ◽

Level 3 ◽

Hydrologic Impacts ◽

A Minor ◽

Global Precipitation Measurement ◽

Global Precipitation

AbstractAfter 5 years in orbit, the Global Precipitation Measurement (GPM) mission has produced enough quality-controlled data to allow the first validation of their precipitation estimates over Spain. High-quality gauge data from the meteorological network of the Spanish Meteorological Agency (AEMET) are used here to validate Integrated Multisatellite Retrievals for GPM (IMERG) level 3 estimates of surface precipitation. While aggregated values compare notably well, some differences are found in specific locations. The research investigates the sources of these discrepancies, which are found to be primarily related to the underestimation of orographic precipitation in the IMERG satellite products, as well as to the number of available gauges in the GPCC gauges used for calibrating IMERG. It is shown that IMERG provides suboptimal performance in poorly instrumented areas but that the estimate improves greatly when at least one rain gauge is available for the calibration process. A main, generally applicable conclusion from this research is that the IMERG satellite-derived estimates of precipitation are more useful (r2 > 0.80) for hydrology than interpolated fields of rain gauge measurements when at least one gauge is available for calibrating the satellite product. If no rain gauges were used, the results are still useful but with decreased mean performance (r2 ≈ 0.65). Such figures, however, are greatly improved if no coastal areas are included in the comparison. Removing them is a minor issue in terms of hydrologic impacts, as most rivers in Spain have their sources far from the coast.

Download Full-text

Cross-validating precipitation datasets in the Indus River basin

Hydrology and Earth System Sciences ◽

10.5194/hess-24-427-2020 ◽

2020 ◽

Vol 24 (1) ◽

pp. 427-450 ◽

Cited By ~ 2

Author(s):

Jean-Philippe Baudouin ◽

Michael Herzog ◽

Cameron A. Petrie

Keyword(s):

River Basin ◽

Cross Validation ◽

Rain Gauge ◽

Reanalysis Data ◽

Satellite Observations ◽

Indus River ◽

Mountain Areas ◽

Temporal Representation ◽

Basin Scale ◽

Indus River Basin

Abstract. Large uncertainty remains about the amount of precipitation falling in the Indus River basin, particularly in the more mountainous northern part. While rain gauge measurements are often considered as a reference, they provide information for specific, often sparse, locations (point observations) and are subject to underestimation, particularly in mountain areas. Satellite observations and reanalysis data can improve our knowledge but validating their results is often difficult. In this study, we offer a cross-validation of 20 gridded datasets based on rain gauge, satellite, and reanalysis data, including the most recent and less studied APHRODITE-2, MERRA2, and ERA5. This original approach to cross-validation alternatively uses each dataset as a reference and interprets the result according to their dependency on the reference. Most interestingly, we found that reanalyses represent the daily variability of precipitation as well as any observational datasets, particularly in winter. Therefore, we suggest that reanalyses offer better estimates than non-corrected rain-gauge-based datasets where underestimation is problematic. Specifically, ERA5 is the reanalysis that offers estimates of precipitation closest to observations, in terms of amounts, seasonality, and variability, from daily to multi-annual scale. By contrast, satellite observations bring limited improvement at the basin scale. For the rain-gauge-based datasets, APHRODITE has the finest temporal representation of the precipitation variability, yet it importantly underestimates the actual amount. GPCC products are the only datasets that include a correction factor of the rain gauge measurements, but this factor likely remains too small. These findings highlight the need for a systematic characterisation of the underestimation of rain gauge measurements.

Download Full-text

First-Year Evaluation of GPM Rainfall over the Netherlands: IMERG Day 1 Final Run (V03D)

Journal of Hydrometeorology ◽

10.1175/jhm-d-16-0087.1 ◽

2016 ◽

Vol 17 (11) ◽

pp. 2799-2814 ◽

Cited By ~ 46

Author(s):

M. F. Rios Gaona ◽

A. Overeem ◽

H. Leijnse ◽

R. Uijlenhoet

Keyword(s):

The Netherlands ◽

Land Surface ◽

Tropical Rainfall Measuring Mission ◽

Ground Truth ◽

Rain Gauge ◽

First Year ◽

Radar Rainfall ◽

High Resolution Data ◽

Precipitation Estimates ◽

Global Precipitation

Abstract The Global Precipitation Measurement (GPM) mission is the successor to the Tropical Rainfall Measuring Mission (TRMM), which orbited Earth for ~17 years. With Core Observatory launched on 27 February 2014, GPM offers global precipitation estimates between 60°N and 60°S at 0.1° × 0.1° resolution every 30 min. Unlike during the TRMM era, the Netherlands is now within the coverage provided by GPM. Here the first year of GPM rainfall retrievals from the 30-min gridded Integrated Multisatellite Retrievals for GPM (IMERG) product Day 1 Final Run (V03D) is assessed. This product is compared against gauge-adjusted radar rainfall maps over the land surface of the Netherlands at 30-min, 24-h, monthly, and yearly scales. These radar rainfall maps are considered to be ground truth. The evaluation of the first year of IMERG operations is done through time series, scatterplots, empirical exceedance probabilities, and various statistical indicators. In general, there is a tendency for IMERG to slightly underestimate (2%) countrywide rainfall depths. Nevertheless, the relative underestimation is small enough to propose IMERG as a reliable source of precipitation data, especially for areas where rain gauge networks or ground-based radars do not offer these types of high-resolution data and availability. The potential of GPM for rainfall estimation in a midlatitude country is confirmed.

Download Full-text

Comparison of Global Precipitation Estimates across a Range of Temporal and Spatial Scales

Journal of Climate ◽

10.1175/jcli-d-15-0618.1 ◽

2016 ◽

Vol 29 (21) ◽

pp. 7773-7795 ◽

Cited By ~ 59

Author(s):

Maria Gehne ◽

Thomas M. Hamill ◽

George N. Kiladis ◽

Kevin E. Trenberth

Keyword(s):

Time Scales ◽

Large Scale ◽

Global Climate ◽

Spatial Scales ◽

Rain Gauge ◽

Random Errors ◽

Climate Data ◽

Precipitation Estimates ◽

Global Precipitation ◽

Daily Time

Abstract Characteristics of precipitation estimates for rate and amount from three global high-resolution precipitation products (HRPPs), four global climate data records (CDRs), and four reanalyses are compared. All datasets considered have at least daily temporal resolution. Estimates of global precipitation differ widely from one product to the next, with some differences likely due to differing goals in producing the estimates. HRPPs are intended to produce the best snapshot of the precipitation estimate locally. CDRs of precipitation emphasize homogeneity over instantaneous accuracy. Precipitation estimates from global reanalyses are dynamically consistent with the large-scale circulation but tend to compare poorly to rain gauge estimates since they are forecast by the reanalysis system and precipitation is not assimilated. Regional differences among the estimates in the means and variances are as large as the means and variances, respectively. Even with similar monthly totals, precipitation rates vary significantly among the estimates. Temporal correlations among datasets are large at annual and daily time scales, suggesting that compensating bias errors at annual and random errors at daily time scales dominate the differences. However, the signal-to-noise ratio at intermediate (monthly) time scales can be large enough to result in high correlations overall. It is shown that differences on annual time scales and continental regions are around 0.8 mm day−1, which corresponds to 23 W m−2. These wide variations in the estimates, even for global averages, highlight the need for better constrained precipitation products in the future.

Download Full-text

Orographic biases in IMERG precipitation estimates in the Ebro River basin (Spain): The effects of rain gauge density and altitude

Atmospheric Research ◽

10.1016/j.atmosres.2020.105068 ◽

2020 ◽

Vol 244 ◽

pp. 105068 ◽

Cited By ~ 3

Author(s):

Andrés Navarro ◽

Eduardo García-Ortega ◽

Andrés Merino ◽

José Luis Sánchez ◽

Francisco J. Tapiador

Keyword(s):

River Basin ◽

Rain Gauge ◽

Ebro River ◽

Precipitation Estimates

Download Full-text

Evaluation of GPM-Era Satellite Precipitation Products on the Southern Slopes of the Central Himalayas Against Rain Gauge Data

Remote Sensing ◽

10.3390/rs12111836 ◽

2020 ◽

Vol 12 (11) ◽

pp. 1836 ◽

Cited By ~ 8

Author(s):

Shankar Sharma ◽

Yingying Chen ◽

Xu Zhou ◽

Kun Yang ◽

Xin Li ◽

...

Keyword(s):

Spatial Distribution ◽

Dynamic Range ◽

Precipitation Amount ◽

Rain Gauge ◽

Central Himalayas ◽

Precipitation Measurement ◽

Precipitation Estimates ◽

New Generation ◽

Global Precipitation Measurement ◽

Global Precipitation

The Global Precipitation Measurement (GPM) mission provides high-resolution precipitation estimates globally. However, their accuracy needs to be accessed for algorithm enhancement and hydro-meteorological applications. This study applies data from 388 gauges in Nepal to evaluate the spatial-temporal patterns presented in recently-developed GPM-Era satellite-based precipitation (SBP) products, i.e., the Integrated Multi-satellite Retrievals for GPM (IMERG), satellite-only (IMERG-UC), the gauge-calibrated IMERG (IMERG-C), the Global Satellite Mapping of Precipitation (GSMaP), satellite-only (GSMaP-MVK), and the gauge-calibrated GSMaP (GSMaP-Gauge). The main results are as follows: (1) GSMaP-Gauge datasets is more reasonable to represent the observed spatial distribution of precipitation, followed by IMERG-UC, GSMaP-MVK, and IMERG-C. (2) The gauge-calibrated datasets are more consistent (in terms of relative root mean square error (RRMSE) and correlation coefficient (R)) than the satellite-only datasets in representing the seasonal dynamic range of precipitation. However, all four datasets can reproduce the seasonal cycle of precipitation, which is predominately governed by the monsoon system. (3) Although all four SBP products underestimate the monsoonal precipitation, the gauge-calibrated IMERG-C yields smaller mean bias than GSMaP-Gauge, while GSMaP-Gauge shows the smaller RRMSE and higher R-value; indicating IMERG-C is more reliable to estimate precipitation amount than GSMaP-Gauge, whereas GSMaP-Gauge presents more reasonable spatial distribution than IMERG-C. Only IMERG-C moderately reproduces the evident elevation-dependent pattern of precipitation revealed by gauge observations, i.e., gradually increasing with elevation up to 2000 m and then decreasing; while GSMaP-Gauge performs much better in representing the gauge observed spatial pattern than others. (4) The GSMaP-Gauge calibrated based on the daily gauge analysis is more consistent with detecting gauge observed precipitation events among the four datasets. The high-intensity related precipitation extremes (95th percentile) are more intense in regions with an elevation below 2500 m; all four SBP datasets have low accuracy (<30%) and mostly underestimated (by >40%) the frequency of extreme events at most of the stations across the country. This work represents the quantification of the new-generation SBP products on the southern slopes of the central Himalayas in Nepal.

Download Full-text

Evaluation of GEOS Precipitation Flagging for SMAP Soil Moisture Retrieval Accuracy

Journal of Hydrometeorology ◽

10.1175/jhm-d-20-0038.1 ◽

2021 ◽

Author(s):

Maheshwari Neelam ◽

Rajat Bindlish ◽

Peggy O’Neill ◽

George J. Huffman ◽

Rolf Reichle ◽

...

Keyword(s):

Soil Moisture ◽

Weather Prediction ◽

Heavy Precipitation ◽

Precipitation Event ◽

Retrieval Accuracy ◽

Accuracy Requirement ◽

Earth Observing System ◽

Precipitation Estimates ◽

Global Precipitation Mission ◽

Global Precipitation

The precipitation flag in the Soil Moisture Active Passive (SMAP) Level 2 passive soil moisture (L2SMP) retrieval product indicates the presence or absence of heavy precipitation at the time of the SMAP overpass. The flag is based on precipitation estimates from the Goddard Earth Observing System (GEOS) Forward Processing numerical weather prediction system. An error in flagging during an active or recent precipitation event can either (1) produce an overestimation of soil moisture due to short-term surface wetting of vegetation and/or surface ponding (if soil moisture retrieval was attempted in the presence of rain), or (2) produce an unnecessary non-retrieval of soil moisture and loss of data (if retrieval is flagged due to an erroneous indication of rain). Satellite precipitation estimates from the Integrated Multi-satellite Retrievals for GPM (IMERG) Version 06 Early Run (latency of ~4 hrs) precipitationCal product are used here to evaluate the GEOS-based precipitation flag in the L2SMP product for both the 6 PM ascending and 6 AM descending SMAP overpasses over the first five years of the mission (2015-2020). Consisting of blended precipitation measurements from the GPM (Global Precipitation Mission) satellite constellation, IMERG is treated as the “truth” when comparing to the GEOS model forecasts of precipitation used by SMAP. Key results include: i) IMERG measurements generally show higher spatial variability than the GEOS forecast precipitation, ii) the IMERG product has a higher frequency of light precipitation amounts, and iii) the effect of incorporating IMERG rainfall measurements in lieu of GEOS precipitation forecasts are minimal on the L2SMP retrieval accuracy (determined vs. in situ soil moisture measurements at core validation sites). Our results indicate that L2SMP retrievals continue to meet the mission’s accuracy requirement (standard deviation of the ubRMSE less than 0.04 m3/m3).

Download Full-text

Evaluation of GPM IMERG Early, Late, and Final rainfall estimates using WegenerNet gauge data in southeastern Austria

Hydrology and Earth System Sciences ◽

10.5194/hess-21-6559-2017 ◽

2017 ◽

Vol 21 (12) ◽

pp. 6559-6572 ◽

Cited By ~ 54

Author(s):

Sungmin O ◽

Ulrich Foelsche ◽

Gottfried Kirchengast ◽

Juergen Fuchsberger ◽

Jackson Tan ◽

...

Keyword(s):

Real Time ◽

Rain Gauge ◽

Time Data ◽

Hourly Rainfall ◽

Precipitation Estimates ◽

Climate Station ◽

Using Data ◽

Global Precipitation ◽

Early Late ◽

Rainfall Estimates

Abstract. The Global Precipitation Measurement (GPM) Integrated Multi-satellite Retrievals for GPM (IMERG) products provide quasi-global (60° N–60° S) precipitation estimates, beginning March 2014, from the combined use of passive microwave (PMW) and infrared (IR) satellites comprising the GPM constellation. The IMERG products are available in the form of near-real-time data, i.e., IMERG Early and Late, and in the form of post-real-time research data, i.e., IMERG Final, after monthly rain gauge analysis is received and taken into account. In this study, IMERG version 3 Early, Late, and Final (IMERG-E,IMERG-L, and IMERG-F) half-hourly rainfall estimates are compared with gauge-based gridded rainfall data from the WegenerNet Feldbach region (WEGN) high-density climate station network in southeastern Austria. The comparison is conducted over two IMERG 0.1°  ×  0.1° grid cells, entirely covered by 40 and 39 WEGN stations each, using data from the extended summer season (April–October) for the first two years of the GPM mission. The entire data are divided into two rainfall intensity ranges (low and high) and two seasons (warm and hot), and we evaluate the performance of IMERG, using both statistical and graphical methods. Results show that IMERG-F rainfall estimates are in the best overall agreement with the WEGN data, followed by IMERG-L and IMERG-E estimates, particularly for the hot season. We also illustrate, through rainfall event cases, how insufficient PMW sources and errors in motion vectors can lead to wide discrepancies in the IMERG estimates. Finally, by applying the method of Villarini and Krajewski (2007), we find that IMERG-F half-hourly rainfall estimates can be regarded as a 25 min gauge accumulation, with an offset of +40 min relative to its nominal time.

Download Full-text