scholarly journals Assessment of IMERG Precipitation Estimates over Europe

2019 ◽  
Vol 11 (21) ◽  
pp. 2470 ◽  
Author(s):  
Navarro ◽  
García-Ortega ◽  
Merino ◽  
Sánchez ◽  
Kummerow ◽  
...  
Keyword(s):  
Poor Performance ◽  
Rain Gauge ◽  
Spatial Gradient ◽  
British Isles ◽  
Mountainous Regions ◽  
Rain Gauges ◽  
Global Precipitation Climatology Centre ◽  
Precipitation Estimates ◽  
The Alps ◽  
Global Precipitation

This paper evaluates Integrated Multi-Satellite Retrievals from GPM (IMERG-F) over Europe for the period 2014–2018 in order to evaluate application of the retrievals to hydrology. IMERG-F is compared with a large pan-European precipitation dataset built on rain gauge stations, i.e., the ENSEMBLES OBServation (E-OBS) gridded dataset. Although there is overall agreement in the spatial distribution of mean precipitation (R2 = 0.8), important discrepancies are revealed in mountainous regions, specifically the Alps, Pyrenees, west coast of the British Isles, Scandinavia, the Iberian and Italian peninsulas, and the Adriatic coastline. The results show that the strongest contributors to poor performance are pixels where IMERG-F has no gauges available for adjustment. If rain gauges are available, IMERG-F yields results similar to those of the surface observations, although the performance varies by region. However, even accounting for gauge adjustment, IMERG-F systematically underestimates precipitation in the Alps and Scandinavian mountains. Conversely, IMERG-F overestimates precipitation in the British Isles, Italian Peninsula, Adriatic coastline, and eastern European plains. Additionally, the research shows that gauge adjustment worsens the spatial gradient of precipitation because of the coarse resolution of Global Precipitation Climatology Centre data.

Assessments of IMERG precipitation over Europe

2020 ◽  
Author(s):  
Andrés Navarro ◽  
Eduardo García-Ortega ◽  
José Luis Sánchez ◽  
Andrés Merino ◽  
Christian Kummerow ◽  
...  
Keyword(s):  
Weather Prediction ◽  
Rain Gauge ◽  
Accurate Estimation ◽  
Spatial Gradient ◽  
British Isles ◽  
Climate Change Research ◽  
Promising Alternative ◽  
Mountainous Regions ◽  
The Alps ◽  
Global Precipitation

<p>Accurate estimation of precipitation is essential in weather prediction, climate change research, and hydrologic applications. However, unlike temperature and pressure, precipitation fields can be spatially patchy and consequently extremely difficult to measure and predict. Many efforts have been made to measure precipitation since the 18th century, but building a global, consistent, and continuous database of rainfall is still challenging. The launch of the Global Precipitation Measurement Core Observatory (GPM-CO) in February 2019 emerged as a promising alternative to measure precipitation at global scale. After five years in orbit, the GPM Mission has produced enough quality-controlled data to allow a validation of their precipitation estimates over Europe.</p><p>This study evaluates Integrated Multi-Satellite Retrievals from GPM (IMERG) over Europe in order to evaluate application of the retrievals to hydrology. IMERG is compared with a pan-European precipitation dataset built on rain gauge stations, the ENSEMBLES OBServation (E-OBS) gridded dataset. Although there is overall agreement in the spatial distribution of mean precipitation (R<sup>2</sup> =0.8), important discrepancies are revealed in mountainous regions, specifically the Pyrenees, the Alps, west coast of the British Isles, Scandinavia, the Italian and Iberian peninsulas, and the Adriatic coastline. The results show that the strongest contributors to poor performance are pixels where IMERG has no gauges available for adjustment. If rain gauges are available, IMERG yields results similar to those of the surface observations, although the performance varies by region. However, even accounting for gauge adjustment, IMERG systematically underestimates precipitation in the Alps and Scandinavian mountains. Conversely, IMERG overestimates precipitation in the British Isles, Adriatic coastline, Italian Peninsula, and eastern European plains. Additionally, the research shows that gauge adjustment worsens the spatial gradient of precipitation because of the coarse resolution of Global Precipitation Climatology Centre data (GPCC).</p>

scholarly journals Impact of Topography and Rainfall Intensity on the Accuracy of IMERG Precipitation Estimates in an Arid Region

2020 ◽  
Vol 13 (1) ◽  
pp. 13
Author(s):  
Mohammed T. Mahmoud ◽  
Safa A. Mohammed ◽  
Mohamed A. Hamouda ◽  
Mohamed M. Mohamed
Keyword(s):  
Rainfall Intensity ◽  
Poor Performance ◽  
Rain Gauge ◽  
Detection Accuracy ◽  
Rain Gauge Data ◽  
Light Rain ◽  
Mountainous Regions ◽  
Data Gaps ◽  
Precipitation Estimates ◽  
Evaluation Techniques

The influence of topographical characteristics and rainfall intensity on the accuracy of satellite precipitation estimates is of importance to the adoption of satellite data for hydrological applications. This study evaluates the three GPM IMERG V05B products over the arid country of Saudi Arabia. Statistical indices quantifying the performance of IMERG products were calculated under three evaluation techniques: seasonal-based, topographical, and rainfall intensity-based. Results indicated that IMERG products have the capability to detect seasons with the highest precipitation values (spring) and seasons with the lowest precipitation (summer). Moreover, results showed that IMERG products performed well under various rainfall intensities, particularly under light rain, which is the most common rainfall in arid regions. Furthermore, IMERG products exhibited high detection accuracy over moderate elevations, whereas it had poor performance over coastal and mountainous regions. Overall, the results confirmed that the performance of the final-run product surpassed the near-real-time products in terms of consistency and errors. IMERG products can improve temporal resolution and play a significant role in filling data gaps in poorly gauged regions. However, due to the errors in IMERG products, it is recommended to use sub-daily rain gauge data in satellite calibration for better rainfall estimation over arid and semiarid regions.

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

2020 ◽  
Vol 21 (2) ◽  
pp. 161-182 ◽  
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.

Evaluation of Monthly Satellite-Derived Precipitation Products over East Africa

2016 ◽  
Vol 17 (10) ◽  
pp. 2555-2573 ◽  
Author(s):  
E. Cattani ◽  
A. Merino ◽  
V. Levizzani
Keyword(s):  
East Africa ◽  
Time Frame ◽  
Rain Gauge ◽  
Satellite Precipitation ◽  
Global Precipitation Climatology Centre ◽  
Time Period ◽  
Precipitation Seasonality ◽  
Precipitation Estimates ◽  
Terrain Elevation ◽  
Global Precipitation

Abstract East Africa experienced in the 2001–11 time period some of the worst drought events to date, culminating in the high-impact drought of 2010/11. Long-term monitoring of precipitation is thus essential, and satellite-based precipitation products can help in coping with the relatively sparse rain gauge ground networks of this area of the world. However, the complex topography and the marked geographic variability of precipitation in the region make precipitation retrieval from satellites problematic and product validation and intercomparison necessary. Six state-of-the-art monthly satellite precipitation products over East Africa during the 2001–09 time frame are evaluated. Eight areas (clusters) are identified by investigating the precipitation seasonality through the Global Precipitation Climatology Centre (GPCC) climatological gauge data. Seasonality was fully reproduced by satellite data in each of the GPCC-identified clusters. Not surprisingly, complex terrain (mountain regions in particular) represents a challenge for satellite precipitation estimates, as demonstrated by the standard deviations of the six-product ensemble. A further confirmation comes from the comparison between satellite estimates and rain gauge measurements as a function of terrain elevation. The 3B42 product performs best, although the satellite–gauge comparative analysis was not completely independent since a few of the products include a rain gauge bias correction.

scholarly journals Assessment of Snowfall Accumulation from Satellite and Reanalysis Products Using SNOTEL Observations in Alaska

2021 ◽  
Vol 13 (15) ◽  
pp. 2922
Author(s):  
Yang Song ◽  
Patrick D. Broxton ◽  
Mohammad Reza Ehsani ◽  
Ali Behrangi
Keyword(s):  
Snow Water Equivalent ◽  
Stage Iv ◽  
Microwave Remote Sensing ◽  
Rain Gauge ◽  
Snow Accumulation ◽  
Correction Factors ◽  
Rainfall Analysis ◽  
Rain Gauges ◽  
Snow Properties ◽  
Precipitation Estimates

The combination of snowfall, snow water equivalent (SWE), and precipitation rate measurements from 39 snow telemetry (SNOTEL) sites in Alaska were used to assess the performance of various precipitation products from satellites, reanalysis, and rain gauges. Observation of precipitation from two water years (2018–2019) of a high-resolution radar/rain gauge data (Stage IV) product was also utilized to give insights into the scaling differences between various products. The outcomes were used to assess two popular methods for rain gauge undercatch correction. It was found that SWE and precipitation measurements at SNOTELs, as well as precipitation estimates based on Stage IV data, are generally consistent and can provide a range within which other products can be assessed. The time-series of snowfall and SWE accumulation suggests that most of the products can capture snowfall events; however, differences exist in their accumulation. Reanalysis products tended to overestimate snow accumulation in the study area, while the current combined passive microwave remote sensing products (i.e., IMERG-HQ) underestimate snowfall accumulation. We found that correction factors applied to rain gauges are effective for improving their undercatch, especially for snowfall. However, no improvement in correlation is seen when correction factors are applied, and rainfall is still estimated better than snowfall. Even though IMERG-HQ has less skill for capturing snowfall than rainfall, analysis using Taylor plots showed that the combined microwave product does have skill for capturing the geographical distribution of snowfall and precipitation accumulation; therefore, bias adjustment might lead to reasonable precipitation estimates. This study demonstrates that other snow properties (e.g., SWE accumulation at the SNOTEL sites) can complement precipitation data to estimate snowfall. In the future, gridded SWE and snow depth data from GlobSnow and Sentinel-1 can be used to assess snowfall and its distribution over broader regions.

scholarly journals Validation of the First Years of GPM Operation over Cyprus

2018 ◽  
Vol 10 (10) ◽  
pp. 1520 ◽  
Author(s):  
Adrianos Retalis ◽  
Dimitris Katsanos ◽  
Filippos Tymvios ◽  
Silas Michaelides
Keyword(s):  
High Resolution ◽  
Prediction Models ◽  
Weather Prediction ◽  
Monthly Basis ◽  
Rain Gauges ◽  
Precipitation Measurement ◽  
Precipitation Estimates ◽  
First Results ◽  
Global Precipitation Measurement ◽  
Global Precipitation

Global Precipitation Measurement (GPM) high-resolution product is validated against rain gauges over the island of Cyprus for a three-year period, starting from April 2014. The precipitation estimates are available in both high temporal (half hourly) and spatial (10 km) resolution and combine data from all passive microwave instruments in the GPM constellation. The comparison performed is twofold: first the GPM data are compared with the precipitation measurements on a monthly basis and then the comparison focuses on extreme events, recorded throughout the first 3 years of GPM’s operation. The validation is based on ground data from a dense and reliable network of rain gauges, also available in high temporal (hourly) resolution. The first results show very good correlation regarding monthly values; however, the correspondence of GPM in extreme precipitation varies from “no correlation” to “high correlation”, depending on case. This study aims to verify the GPM rain estimates, since such a high-resolution dataset has numerous applications, including the assimilation in numerical weather prediction models and the study of flash floods with hydrological models.

Satellite soil moisture improves rainfall just where needed

2020 ◽  
Author(s):  
Luca Brocca ◽  
Stefania Camici ◽  
Christian Massari ◽  
Luca Ciabatta ◽  
Paolo Filippucci ◽  
...  
Keyword(s):  
Soil Moisture ◽  
River Discharge ◽  
Large Scale ◽  
River Flow ◽  
Global Scale ◽  
Rain Gauge ◽  
Rainfall Runoff ◽  
Global Precipitation Climatology Centre ◽  
Satellite Rainfall ◽  
Global Precipitation

<p>Soil moisture is a fundamental variable in the water and energy cycle and its knowledge in many applications is crucial. In the last decade, some authors have proposed the use of satellite soil moisture for estimating and improving rainfall, doing hydrology backward. From this research idea, several studies have been published and currently preoperational satellite rainfall products exploiting satellite soil moisture products have been made available.</p><p>The assessment of such products on a global scale has revealed an important result, i.e., the soil moisture based products perform better than state of the art products exactly over regions in which the data are needed: Africa and South America. However, over these areas the assessment against rain gauge observations is problematic and independent approaches are needed to assess the quality of such products and their potential benefit in hydrological applications. On this basis, the use of the satellite rainfall products as input into rainfall-runoff models, and their indirect assessment through river discharge observations is an alternative and valuable approach for evaluating their quality.</p><p>For this study, a newly developed large scale dataset of river discharge observations over 500+ basins throughout Africa has been exploited. Based on such unique dataset, a large scale assessment of multiple near real time satellite rainfall products has been performed: (1) the Early Run version of the Integrated Multi-Satellite Retrievals for GPM (Global Precipitation Measurement), IMERG Early Run, (2) SM2RAIN-ASCAT (https://doi.org/10.5281/zenodo.3405563), and (3) GPM+SM2RAIN (http://doi.org/10.5281/zenodo.3345323). Additionally, gauge-based and reanalysis rainfall products have been considered, i.e., (4) the Global Precipitation Climatology Centre (GPCC), and (5) the latest European Centre for Medium-Range Weather Forecasts reanalysis, ERA5. As rainfall-runoff model, the semi-distributed MISDc (Modello Idrologico Semi-Distribuito in continuo) model has been employed in the period 2007-2018 at daily temporal scale.</p><p>First results over a part of the dataset reveal the great value of satellite soil moisture products in improving satellite rainfall estimates for river flow prediction in Africa. Such results highlight the need to exploit such products for operational systems in Africa addressed to the mitigation of the flood risk and water resources management.</p>

scholarly journals Precipitation Analysis over the French Alps Using a Variational Approach and Study of Potential Added Value of Ground-Based Radar Observations

2017 ◽  
Vol 18 (5) ◽  
pp. 1425-1451 ◽  
Author(s):  
Camille Birman ◽  
Fatima Karbou ◽  
Jean-François Mahfouf ◽  
Matthieu Lafaysse ◽  
Yves Durand ◽  
...  
Keyword(s):  
Snow Depth ◽  
Positive Impact ◽  
Weather Prediction ◽  
Rain Gauge ◽  
Added Value ◽  
Background Information ◽  
Time Step ◽  
Rain Gauges ◽  
Weather Radars ◽  
Precipitation Estimates

Abstract A one-dimensional variational data assimilation (1DVar) method to retrieve profiles of precipitation in mountainous terrain is described. The method combines observations from the French Alpine region rain gauges and precipitation estimates from weather radars with background information from short-range numerical weather prediction forecasts in an optimal way. The performance of this technique is evaluated using measurements of precipitation and of snow depth during two years (2012/13 and 2013/14). It is shown that the 1DVar model allows an effective assimilation of measurements of different types, including rain gauge and radar-derived precipitation. The use of radar-derived precipitation rates over mountains to force the numerical snowpack model Crocus significantly reduces the bias and standard deviation with respect to independent snow depth observations. The improvement is particularly significant for large rainfall or snowfall events, which are decisive for avalanche hazard forecasting. The use of radar-derived precipitation rates at an hourly time step improves the time series of precipitation analyses and has a positive impact on simulated snow depths.

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

2016 ◽  
Vol 17 (11) ◽  
pp. 2799-2814 ◽  
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.

scholarly journals So, How Much of the Earth’s Surface Is Covered by Rain Gauges?

2017 ◽  
Vol 98 (1) ◽  
pp. 69-78 ◽  
Author(s):  
Chris Kidd ◽  
Andreas Becker ◽  
George J. Huffman ◽  
Catherine L. Muller ◽  
Paul Joe ◽  
...  
Keyword(s):  
Data Access ◽  
Rain Gauges ◽  
Temporal Sampling ◽  
Global Precipitation Climatology Centre ◽  
Integration Period ◽  
Wide Range ◽  
Equivalent Area ◽  
Tennis Court ◽  
Global Precipitation ◽  
Data Latency

Abstract The measurement of global precipitation, both rainfall and snowfall, is critical to a wide range of users and applications. Rain gauges are indispensable in the measurement of precipitation, remaining the de facto standard for precipitation information across Earth’s surface for hydrometeorological purposes. However, their distribution across the globe is limited: over land their distribution and density is variable, while over oceans very few gauges exist and where measurements are made, they may not adequately reflect the rainfall amounts of the broader area. Critically, the number of gauges available, or appropriate for a particular study, varies greatly across the Earth owing to temporal sampling resolutions, periods of operation, data latency, and data access. Numbers of gauges range from a few thousand available in near–real time to about 100,000 for all “official” gauges, and to possibly hundreds of thousands if all possible gauges are included. Gauges routinely used in the generation of global precipitation products cover an equivalent area of between about 250 and 3,000 m2. For comparison, the center circle of a soccer pitch or tennis court is about 260 m2. Although each gauge should represent more than just the gauge orifice, autocorrelation distances of precipitation vary greatly with regime and the integration period. Assuming each Global Precipitation Climatology Centre (GPCC)–available gauge is independent and represents a surrounding area of 5-km radius, this represents only about 1% of Earth’s surface. The situation is further confounded for snowfall, which has a greater measurement uncertainty.

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