| publications-1551 |
PEER REVIEWED ARTICLE |
2016 |
Yiwen Mei , Efthymios I. Nikolopoulos , Emmanouil N. Anagnostou , Marco Borga |
Evaluating Satellite Precipitation Error Propagation in Runoff Simulations of Mountainous Basins |
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10.1175/jhm-d-15-0081.1 |
Simulation & Modeling |
Groundwater |
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AbstractThis study investigates the error characteristics of six quasi-global satellite precipitation products and their error propagation in flow simulations for a range of mountainous basin scales (255–6967 km2) and two different periods (May–August and September–November) in northeast Italy. Statistics describing the systematic and random error, the temporal similarity, and error ratios between precipitation and runoff are presented. Overall, strong over-/underestimation associated with the near-real-time 3B42/Climate Prediction Center morphing technique (CMORPH) products is shown. Results suggest positive correlation between the systematic error and basin elevation. Performance evaluation of flow simulations yields a higher degree of consistency for the moderate to large basin scales and the May–August period. Gauge adjustment for the different satellite products is shown to moderate their error magnitude and increase their correlation with reference precipitation and streamflow simulations. Moreover, ratios of precipitation to streamflow simulation error metrics show dependencies in terms of magnitude and variability. Random error and temporal dissimilarity are shown to reduce from basin-average rainfall to the streamflow simulations, while the systematic error exhibits no clear pattern in the rainfall–runoff transformation. |
603608 |
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| publications-1552 |
PEER REVIEWED ARTICLE |
2017 |
Kengo Miyaoka , Alexander Gruber , Francesca Ticconi , Sebastian Hahn , Wolfgang Wagner , Julia Figa-Saldana , Craig Anderson |
Triple Collocation Analysis of Soil Moisture From Metop-A ASCAT and SMOS Against JRA-55 and ERA-Interim |
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10.1109/jstars.2016.2632306 |
Simulation & Modeling |
Groundwater |
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No abstract available |
603608 |
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| publications-1553 |
PEER REVIEWED ARTICLE |
2016 |
Rene Orth , Emanuel Dutra , Isabel F. Trigo , Gianpaolo Balsamo |
Advancing land surface model development with satellite-based Earth observations |
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10.5194/hess-2016-628 |
Predictive Analytics |
Precipitation & Ecological Systems |
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Abstract. The land surface forms an essential part of the climate system. It interacts with the atmosphere through the exchange of water and energy and hence influences weather and climate, as well as their predictability. Correspondingly, the land surface model (LSM) is an essential part of any weather forecasting system. LSMs rely on partly poorly constrained parameters, due to sparse land surface observations. With the use of newly available land surface temperature observations, we show in this study that novel satellite-derived datasets help to improve LSM configuration, and hence can contribute to improved weather predictability. We use the Hydrology Tiled ECMWF Scheme of Surface Exchanges over Land (HTESSEL) and validate it comprehensively against an array of Earth observation reference datasets, including the new land surface temperature product. This reveals satisfactory model performance in terms of hydrology, but poor performance in terms of land surface temperature. This is due to inconsistencies of process representations in the model as identified from an analysis of perturbed parameter simulations. We show that HTESSEL can be more robustly calibrated with multiple instead of single reference datasets as this mitigates the impact of the structural inconsistencies. Finally, performing coupled global weather forecasts we find that a more robust calibration of HTESSEL also contributes to improved weather forecast skills. In summary, new satellite-based Earth observations are shown to enhance the multi-dataset calibration of LSMs, thereby improving the representation of insufficiently captured processes, advancing weather predictability and understanding of climate system feedbacks. |
603608 |
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| publications-1554 |
PEER REVIEWED ARTICLE |
2017 |
Jean-Francois Rysman , Chantal Claud , Julien Delanoe |
Monitoring Deep Convection and Convective Overshooting From 60° S to 60° N Using MHS: A Cloudsat/CALIPSO-Based Assessment |
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10.1109/lgrs.2016.2631725 |
IoT & Sensors |
Precipitation & Ecological Systems |
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No abstract available |
603608 |
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| publications-1555 |
PEER REVIEWED ARTICLE |
2016 |
Jean-François Rysman , Ségolène Berthou , Chantal Claud , Philippe Drobinski , Jean-Pierre Chaboureau , Julien Delanoë |
Potential of microwave observations for the evaluation of rainfall and convection in a regional climate model in the frame of HyMeX and MED-CORDEX |
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10.1007/s00382-016-3203-7 |
Data Management & Analytics |
Groundwater |
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No abstract available |
603608 |
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| publications-1556 |
PEER REVIEWED ARTICLE |
2015 |
Ming Pan , Colby K. Fisher , Nathaniel W. Chaney , Wang Zhan , Wade T. Crow , Filipe Aires , Dara Entekhabi , Eric F. Wood |
Triple collocation: Beyond three estimates and separation of structural/non-structural errors |
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10.1016/j.rse.2015.10.028 |
Data Management & Analytics |
Soil Moisture |
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No abstract available |
603608 |
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| publications-1557 |
PEER REVIEWED ARTICLE |
2016 |
Paolo Sanò , Giulia Panegrossi , Daniele Casella , Anna C. Marra , Francesco Di Paola , Stefano Dietrich |
The new Passive microwave Neural network Precipitation Retrieval (PNPR) algorithm for the cross-track scanning ATMS radiometer: description and verification study over Europe and Africa using GPM and TRMM spaceborne radars |
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10.5194/amt-9-5441-2016 |
Simulation & Modeling |
Soil Moisture |
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Abstract. The objective of this paper is to describe the development and evaluate the performance of a completely new version of the Passive microwave Neural network Precipitation Retrieval (PNPR v2), an algorithm based on a neural network approach, designed to retrieve the instantaneous surface precipitation rate using the cross-track Advanced Technology Microwave Sounder (ATMS) radiometer measurements. This algorithm, developed within the EUMETSAT H-SAF program, represents an evolution of the previous version (PNPR v1), developed for AMSU/MHS radiometers (and used and distributed operationally within H-SAF), with improvements aimed at exploiting the new precipitation-sensing capabilities of ATMS with respect to AMSU/MHS. In the design of the neural network the new ATMS channels compared to AMSU/MHS, and their combinations, including the brightness temperature differences in the water vapor absorption band, around 183 GHz, are considered. The algorithm is based on a single neural network, for all types of surface background, trained using a large database based on 94 cloud-resolving model simulations over the European and the African areas. The performance of PNPR v2 has been evaluated through an intercomparison of the instantaneous precipitation estimates with co-located estimates from the TRMM Precipitation Radar (TRMM-PR) and from the GPM Core Observatory Ku-band Precipitation Radar (GPM-KuPR). In the comparison with TRMM-PR, over the African area the statistical analysis was carried out for a 2-year (2013–2014) dataset of coincident observations over a regular grid at 0.5° × 0.5° resolution. The results have shown a good agreement between PNPR v2 and TRMM-PR for the different surface types. The correlation coefficient (CC) was equal to 0.69 over ocean and 0.71 over vegetated land (lower values were obtained over arid land and coast), and the root mean squared error (RMSE) was equal to 1.30 mm h−1 over ocean and 1.11 mm h−1 over vegetated land. The results showed a slight tendency to underestimate moderate to high precipitation, mostly over land, and overestimate moderate to light precipitation over ocean. Similar results were obtained for the comparison with GPM-KuPR over the European area (15 months, from March 2014 to May 2015 of coincident overpasses) with slightly lower CC (0.59 over vegetated land and 0.57 over ocean) and RMSE (0.82 mm h−1 over vegetated land and 0.71 mm h−1 over ocean), confirming a good agreement also between PNPR v2 and GPM-KuPR. The performance of PNPR v2 over the African area was also compared to that of PNPR v1. PNPR v2 has higher R over the different surfaces, with generally better estimation of low precipitation, mostly over ocean, thanks to improvements in the design of the neural network and also to the improved capabilities of ATMS compared to AMSU/MHS. Both versions of PNPR algorithm have shown a general consistency with the TRMM-PR. |
603608 |
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| publications-1558 |
PEER REVIEWED ARTICLE |
2016 |
Albert I. J. M. Van Dijk , G. Robert Brakenridge , Albert J. Kettner , Hylke E. Beck , Tom De Groeve , Jaap Schellekens |
River gauging at global scale using optical and passive microwave remote sensing |
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10.1002/2015wr018545 |
Data Management & Analytics |
Data Management & Analytics |
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AbstractRecent discharge observations are lacking for most rivers globally. Discharge can be estimated from remotely sensed floodplain and channel inundation area, but there is currently no method that can be automatically extended to many rivers. We examined whether automated monitoring is feasible by statistically relating inundation estimates from moderate to coarse (>0.05°) resolution remote sensing to monthly station discharge records. Inundation extents were derived from optical MODIS data and passive microwave sensors, and compared to monthly discharge records from over 8000 gauging stations and satellite altimetry observations for 442 reaches of large rivers. An automated statistical method selected grid cells to construct “satellite gauging reaches” (SGRs). MODIS SGRs were generally more accurate than passive microwave SGRs, but there were complementary strengths. The rivers widely varied in size, regime, and morphology. As expected performance was low (R < 0.7) for many (86%), often small or regulated, rivers, but 1263 successful SGRs remained. High monthly discharge variability enhanced performance: a standard deviation of 100–1000 m3 s−1 yielded ca. 50% chance of R > 0.6. The best results (R > 0.9) were obtained for large unregulated lowland rivers, particularly in tropical and boreal regions. Relatively poor results were obtained in arid regions, where flow pulses are few and recede rapidly, and in temperate regions, where many rivers are modified and contained. Provided discharge variations produce clear changes in inundated area and gauge records are available for part of the satellite record, SGRs can retrieve monthly river discharge values back to around 1998 and up to present. |
603608 |
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| publications-1559 |
PEER REVIEWED ARTICLE |
2016 |
Simon Zwieback , Chun-Hsu Su , Alexander Gruber , Wouter A. Dorigo , Wolfgang Wagner |
The Impact of Quadratic Nonlinear Relations between Soil Moisture Products on Uncertainty Estimates from Triple Collocation Analysis and Two Quadratic Extensions |
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10.1175/jhm-d-15-0213.1 |
Data Management & Analytics |
Soil Moisture |
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Abstract The error characterization of soil moisture products, for example, obtained from microwave remote sensing data, is a key requirement for using these products in applications like numerical weather prediction. The error variance and root-mean-square error are among the most popular metrics: they can be estimated consistently for three datasets using triple collocation (TC) without assuming any dataset to be free of errors. This technique can account for additive and multiplicative biases; that is, it assumes that the three products are linearly related. However, its susceptibility to nonlinear relations (e.g., due to sensor saturation and scale mismatch) has not been addressed. Here, a simulation study investigates the impact of quadratic relations on the TC error estimates [also when the products are first rescaled using the nonlinear cumulative distribution function (CDF) matching technique] and on those by two novel methods. These methods—based on error-in-variables regression and probabilistic factor analysis—extend standard TC by also accounting for nonlinear relations using quadratic polynomials. The relative differences between the error estimates of the ASCAT remotely sensed product by the quadratic and the linear methods are predominantly smaller than 10% in a case study based on remotely sensed, reanalysis, and in situ measured soil moisture over the contiguous United States. Exceptions with larger discrepancies indicate that nonlinear relations can pose a challenge to traditional TC analyses, as the simulations show they can introduce biases of either sign. In such cases, the use of nonlinear methods may complement traditional approaches for the error characterization of soil moisture products. |
603608 |
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| publications-1560 |
PEER REVIEWED ARTICLE |
2017 |
A.C. Marra , F. Porcù , L. Baldini , M. Petracca , D. Casella , S. Dietrich , A. Mugnai , P. Sanò , G. Vulpiani , G. Panegrossi |
Observational analysis of an exceptionally intense hailstorm over the Mediterranean area: Role of the GPM Core Observatory |
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10.1016/j.atmosres.2017.03.019 |
Simulation & Modeling |
River Basins |
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No abstract available |
603608 |
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