Enhanced large-scale validation of satellite-based land rainfall products

2020 ◽  
Author(s):  
F. Chen ◽  
W. T. Crow ◽  
L. Ciabatta ◽  
P. Filippucci ◽  
G. Panegrossi ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Large Scale ◽  
Scale Validation ◽  
Daily Rainfall ◽  
Rain Gauge ◽  
Overlapping Data ◽  
Precipitation Estimates ◽  
Collocation Approach ◽  
Objective Basis ◽  
Independent Product

AbstractSatellite-based precipitation estimates (SPEs) are generally validated using ground-based rain gauge or radar observations. However, in poorly instrumented regions, uncertainty in these references can lead to biased assessments of SPE accuracy. As a result, at regional or continental scales, an objective basis to evaluate SPEs is currently lacking. Here, we evaluate the potential for large-scale, spatially continuous evaluation of SPEs over land via the application of collocation-based techniques (i.e., triple collocation (TC) and quadruple collocation (QC) analyses). Our collocation approach leverages the SM2RAIN (Soil Moisture to Rain) rainfall product, derived from the time series analysis of satellite-based soil moisture retrievals, in combination with independent rainfall datasets acquired from ground observations and climate reanalysis to validate four years of the EUMETSAT (European Organisation for the Exploitation of Meteorological Satellites) H-SAF (Satellite Application Facility on Support to Operational Hydrology and Water Management) H23 daily rainfall product. Large-scale maps of the H23 correlation metric are generated using both TC and QC analyses. Results demonstrate that the SM2RAIN product is a uniquely valuable independent product for collocation analyses, as other available large-scale rainfall datasets are often based on overlapping data sources and algorithms. In particular, the availability of SM2RAIN facilitates the large-scale evaluation of SPE products like H23 – even in areas lacking adequate ground-based observations to apply traditional validation approaches.

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

2016 ◽  
Vol 29 (21) ◽  
pp. 7773-7795 ◽  
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.

scholarly journals Evaluation of radar-gauge merging methods for quantitative precipitation estimates

2008 ◽  
Vol 5 (5) ◽  
pp. 2975-3003 ◽  
Author(s):  
E. Goudenhoofdt ◽  
L. Delobbe
Keyword(s):  
Bias Correction ◽  
Mean Field ◽  
Daily Rainfall ◽  
Absolute Error ◽  
Rain Gauge ◽  
Radar Observations ◽  
Geostatistical Methods ◽  
Precipitation Estimates ◽  
Field Bias ◽  
Quantitative Precipitation Estimates

Abstract. Accurate quantitative precipitation estimates are of crucial importance for hydrological studies and applications. When spatial precipitation fields are required, rain gauge measurements are often combined with weather radar observations. In this paper, we evaluate several radar-gauge merging methods with various degrees of complexity: from mean field bias correction to geostatical merging techniques. The study area is the Walloon region of Belgium, which is mostly located in the Meuse catchment. Observations from a C-band Doppler radar and a dense rain gauge network are used to retrieve daily rainfall accumulations over this area. The relative performance of the different merging methods are assessed through a comparison against daily measurements from an independent gauge network. A 3-year verification is performed using several statistical quality parameters. It appears that the geostatistical merging methods perform best with the mean absolute error decreasing by 40% with respect to the original data. A mean field bias correction still achieves a reduction of 25%. A seasonal analysis shows that the benefit of using radar observations is particularly significant during summer. The effect of the network density on the performance of the methods is also investigated. For this purpose, a simple approach to remove gauges from a network is proposed. The analysis reveals that the sensitivity is relatively high for the geostatistical methods but rather small for the simple methods. The geostatistical methods give the best results for all network densities except for a very low density of 1 gauge per 500 km2 where a range-dependent adjustment complemented with a static local bias correction performs best.

scholarly journals Evaluation of radar-gauge merging methods for quantitative precipitation estimates

2009 ◽  
Vol 13 (2) ◽  
pp. 195-203 ◽  
Author(s):  
E. Goudenhoofdt ◽  
L. Delobbe
Keyword(s):  
Bias Correction ◽  
Mean Field ◽  
Daily Rainfall ◽  
Absolute Error ◽  
Rain Gauge ◽  
Network Density ◽  
Radar Observations ◽  
Precipitation Estimates ◽  
Field Bias ◽  
Quantitative Precipitation Estimates

Abstract. Accurate quantitative precipitation estimates are of crucial importance for hydrological studies and applications. When spatial precipitation fields are required, rain gauge measurements are often combined with weather radar observations. In this paper, we evaluate several radar-gauge merging methods with various degrees of complexity: from mean field bias correction to geostatistical merging techniques. The study area is the Walloon region of Belgium, which is mostly located in the Meuse catchment. Observations from a C-band Doppler radar and a dense rain gauge network are used to estimate daily rainfall accumulations over this area. The relative performance of the different merging methods are assessed through a comparison against daily measurements from an independent gauge network. A 4-year verification is performed using several statistical quality parameters. It appears that the geostatistical merging methods perform best with the mean absolute error decreasing by 40% with respect to the original data. A mean field bias correction still achieves a reduction of 25%. A seasonal analysis shows that the benefit of using radar observations is particularly significant during summer. The effect of the network density on the performance of the methods is also investigated. For this purpose, a simple approach to remove gauges from a network is proposed. The analysis reveals that the sensitivity is relatively high for the geostatistical methods but rather small for the simple methods. The geostatistical merging methods give the best results for all tested network densities and their relative benefit increases with the network density.

Modulation of the Diurnal Cycle of Rainfall Associated with the MJO Observed by a Dense Hourly Rain Gauge Network at Sarawak, Borneo

2013 ◽  
Vol 26 (13) ◽  
pp. 4858-4875 ◽  
Author(s):  
Hironari Kanamori ◽  
Tetsuzo Yasunari ◽  
Koichiro Kuraji
Keyword(s):  
Diurnal Cycle ◽  
Large Scale ◽  
Coastal Region ◽  
Daily Rainfall ◽  
Sea Breeze ◽  
Moisture Flux ◽  
Rain Gauge ◽  
Flux Divergence ◽  
Interior Region ◽  
Rain Gauge Network

Abstract This study investigates spatiotemporal characteristics of the diurnal cycle (DC) of rainfall over Sarawak in northwest Borneo Island, associated with large-scale intraseasonal disturbances represented by the Madden–Julian oscillation (MJO). This is accomplished using a dense hourly rain gauge network and satellite data. The spatial pattern of the DC is classified into two major groups, coastal and interior regions, based on remarkable differences in rainfall peak times and amplitudes. Amplitudes of the DC and daily rainfall amount increase in active MJO phases at all sites, but the MJO has a stronger effect in the coastal region than the interior region. This modulation of rainfall by the MJO disturbance is largely attributed to rainfall frequency in the interior region, but to both frequency and intensity of rainfall in the coastal region. The low-level westerly wind anomaly enhances convergence, the land–sea breeze, and a midnight rainfall peak in the coastal region during the active MJO phase. Analysis of moisture flux divergence and moist static instability suggests the different dynamics of this modulation of the DC between coastal and interior regions.

scholarly journals Effect of spatial distribution of daily rainfall on interior catchment response of a distributed hydrological model

2007 ◽  
Vol 11 (2) ◽  
pp. 677-693 ◽  
Author(s):  
J. M. Schuurmans ◽  
M. F. P. Bierkens
Keyword(s):  
Soil Moisture ◽  
Spatial Variability ◽  
Groundwater Level ◽  
Large Scale ◽  
Hydrological Model ◽  
Daily Rainfall ◽  
Distributed Hydrological Model ◽  
General Behaviour ◽  
Average Rainfall ◽  
Insight Into

Abstract. We investigate the effect of spatial variability of daily rainfall on soil moisture, groundwater level and discharge using a physically-based, fully-distributed hydrological model. This model is currently in use with the district water board and is considered to represent reality. We focus on the effect of rainfall spatial variability on day-to-day variability of the interior catchment response, as well as on its effect on the general hydrological behaviour of the catchment. The study is performed in a flat rural catchment (135 km2) in the Netherlands, where the climate is semi-humid (average precipitation 800 mm/year, evapotranspiration 550 mm/year) and rainfall is predominantly stratiform (i.e. large scale). Both range-corrected radar data (resolution 2.5×2.5 km2) as well as data from a dense network of 30 raingauges are used, observed for the period March–October 2004. Eight different rainfall scenarios, either spatially distributed or spatially uniform, are used as input for the hydrological model. The main conclusions from this study are: (i) using a single raingauge as rainfall input carries a great risk for the prediction of discharge, groundwater level and soil moisture, especially if the raingauge is situated outside the catchment; (ii) taking into account the spatial variability of rainfall instead of using areal average rainfall as input for the model is needed to get insight into the day-to-day spatial variability of discharge, groundwater level and soil moisture content; (iii) to get insight into the general behaviour of the hydrological system it is sufficient to use correct predictions of areal average rainfall over the catchment.

PROTOTIPE APLIKASI PENYIRAMAN TANAMAN MENGGUNAKAN SENSOR KELEMBABAN TANAH BERBASIS MICRO CONTOLLER ATMEGA 328

2019 ◽  
Vol 5 (1) ◽  
pp. 97-106
Author(s):  
Rudi Budi Agung ◽  
Muhammad Nur ◽  
Didi Sukayadi
Keyword(s):  
Soil Moisture ◽  
Growth And Development ◽  
Large Scale ◽  
Simple Method ◽  
Moisture Sensor ◽  
Sensor Development ◽  
Soil Moisture Sensor ◽  
Application Systems ◽  
Harvesting Systems ◽  
Working Principle

The Indonesian country which is famous for its tropical climate has now experienced a shift in two seasons (dry season and rainy season). This has an impact on cropping and harvesting systems among farmers. In large scale this is very influential considering that farmers in Indonesia are stilldependent on rainfall which results in soil moisture. Some types of plants that are very dependent on soil moisture will greatly require rainfall or water for growth and development. Through this research, researchers tried to make a prototype application for watering plants using ATMEGA328 microcontroller based soil moisture sensor. Development of application systems using the prototype method as a simple method which is the first step and can be developed again for large scale. The working principle of this prototype is simply that when soil moisture reaches a certainthreshold (above 56%) then the system will work by activating the watering system, if it is below 56% the system does not work or in other words soil moisture is considered sufficient for certain plant needs.

scholarly journals Mapping High Spatiotemporal-Resolution Soil Moisture by Upscaling Sparse Ground-Based Observations Using a Bayesian Linear Regression Method for Comparison with Microwave Remotely Sensed Soil Moisture Products

2021 ◽  
Vol 13 (2) ◽  
pp. 228
Author(s):  
Jian Kang ◽  
Rui Jin ◽  
Xin Li ◽  
Yang Zhang
Keyword(s):  
Soil Moisture ◽  
Large Scale ◽  
Spatial Scales ◽  
Microwave Remote Sensing ◽  
In Situ Measurements ◽  
Spatial Density ◽  
Linear Regression Method ◽  
Spatiotemporal Resolution ◽  
Pixel Scale

In recent decades, microwave remote sensing (RS) has been used to measure soil moisture (SM). Long-term and large-scale RS SM datasets derived from various microwave sensors have been used in environmental fields. Understanding the accuracies of RS SM products is essential for their proper applications. However, due to the mismatched spatial scale between the ground-based and RS observations, the truth at the pixel scale may not be accurately represented by ground-based observations, especially when the spatial density of in situ measurements is low. Because ground-based observations are often sparsely distributed, temporal upscaling was adopted to transform a few in situ measurements into SM values at a pixel scale of 1 km by introducing the temperature vegetation dryness index (TVDI) related to SM. The upscaled SM showed high consistency with in situ SM observations and could accurately capture rainfall events. The upscaled SM was considered as the reference data to evaluate RS SM products at different spatial scales. In regard to the validation results, in addition to the correlation coefficient (R) of the Soil Moisture Active Passive (SMAP) SM being slightly lower than that of the Climate Change Initiative (CCI) SM, SMAP had the best performance in terms of the root-mean-square error (RMSE), unbiased RMSE and bias, followed by the CCI. The Soil Moisture and Ocean Salinity (SMOS) products were in worse agreement with the upscaled SM and were inferior to the R value of the X-band SM of the Advanced Microwave Scanning Radiometer 2 (AMSR2). In conclusion, in the study area, the SMAP and CCI SM are more reliable, although both products were underestimated by 0.060 cm3 cm−3 and 0.077 cm3 cm−3, respectively. If the biases are corrected, then the improved SMAP with an RMSE of 0.043 cm3 cm−3 and the CCI with an RMSE of 0.039 cm3 cm−3 will hopefully reach the application requirement for an accuracy with an RMSE less than 0.040 cm3 cm−3.

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