Comprehensive evaluation of satellite-based and reanalysis soil moisture products using in situ observations over China

Mapping Intimacies ◽

10.5194/hess-2020-611 ◽

2021 ◽

Author(s):

Xiaolu Ling ◽

Ying Huang ◽

Weidong Guo ◽

Yixin Wang ◽

Chaorong Chen ◽

...

Keyword(s):

Remote Sensing ◽

Soil Moisture ◽

Mainland China ◽

Severe Drought ◽

Weather Forecasts ◽

European Centre ◽

Drought Conditions ◽

Medium Range

Abstract. Soil moisture (SM) plays a critical role in the water and energy cycles of the earth system; consequently, a long-term SM product with high quality is urgently needed. In this study, five SM products, including one microwave remote sensing product [European Space Agency's Climate Change Initiative (ESA CCI)] and four reanalysis datasets [European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis-Interim (ERAI), National Centers for Environmental Prediction (NCEP), the Twentieth Century Reanalysis Project from National Oceanic and Atmospheric Administration (NOAA) and the European Centre for Medium-Range Weather Forecasts Reanalysis 5 (ERA5)], are systematically evaluated using in situ measurements during 1981–2013 in four climate regions at different timescales over mainland China. The results show that ESA CCI is closest to the observations in terms of both the spatial distributions and magnitude of the monthly SM. All reanalysis products tend to overestimate soil moisture in all regions but have higher correlations than the remote sensing product except in Northwest China. The largest inconsistency is found in southern Northeast China, with a relative RMSE value larger than 0.1. However, none of the products can well reproduce the trends of interannual anomalies. The largest relative bias of 44.6 % is found for the ERAI SM product under severe drought conditions, and the lowest relative biases of 4.7 % and 9.5 % are found for the ESA CCI SM product under severe drought conditions and the NCEP SM product under normal conditions, respectively. As decomposing mean square errors in all the products suggests that the bias terms are the dominant contribution, the ESA CCI SM product is a good option for long-term hydrometeorological applications in mainland China. ERA5 is also a promising product, which is attributed to the incorporation of more observations. This long-term intercomparison study provides clues for SM product enhancement and further hydrological applications.

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Comprehensive evaluation of satellite-based and reanalysis soil moisture products using in situ observations over China

Hydrology and Earth System Sciences ◽

10.5194/hess-25-4209-2021 ◽

2021 ◽

Vol 25 (7) ◽

pp. 4209-4229

Author(s):

Xiaolu Ling ◽

Ying Huang ◽

Weidong Guo ◽

Yixin Wang ◽

Chaorong Chen ◽

...

Keyword(s):

Remote Sensing ◽

Soil Moisture ◽

Chinese Mainland ◽

Relative Bias ◽

Northwestern China ◽

Mean Square ◽

The Chinese Mainland ◽

Ecmwf Reanalysis

Abstract. Soil moisture (SM) plays a critical role in the water and energy cycles of the Earth system; consequently, a long-term SM product with high quality is urgently needed. In this study, five SM products, including one microwave remote sensing product – the European Space Agency's Climate Change Initiative (ESA CCI) – and four reanalysis data sets – European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis – Interim (ERA-Interim), National Centers for Environmental Prediction (NCEP), the 20th Century Reanalysis Project from National Oceanic and Atmospheric Administration (NOAA), and the ECMWF Reanalysis 5 (ERA5) – are systematically evaluated using in situ measurements during 1981–2013 in four climate regions at different timescales over the Chinese mainland. The results show that ESA CCI is closest to the observations in terms of both the spatial distributions and magnitude of the monthly SM. All reanalysis products tend to overestimate soil moisture in all regions but have higher correlations than the remote sensing product except in Northwest China. The largest inconsistency is found in southern Northeast China region, with an unbiased root mean square error (ubRMSE) value larger than 0.04. However, all products exhibit certain weaknesses in representing the interannual variation in SM. The largest relative bias of 144.4 % is found for the ERA-Interim SM product under extreme and severe wet conditions in northeastern China, and the lowest relative bias is found for the ESA CCI SM product, with the minimum of 0.48 % under extreme and severe wet conditions in northwestern China. Decomposing mean square errors suggests that the bias terms are the dominant contribution for all products, and the correlation term is large for ESA CCI. As a result, the ESA CCI SM product is a good option for long-term hydrometeorological applications on the Chinese mainland. ERA5 is also a promising product, especially in northern and northwestern China in terms of low bias and high correlation coefficient. This long-term intercomparison study provides clues for SM product enhancement and further hydrological applications.

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Evaluation of Global Surface Water Temperature Data Sets for Use in Passive Remote Sensing of Soil Moisture

Remote Sensing ◽

10.3390/rs13101872 ◽

2021 ◽

Vol 13 (10) ◽

pp. 1872

Author(s):

Runze Zhang ◽

Steven Chan ◽

Rajat Bindlish ◽

Venkataraman Lakshmi

Keyword(s):

Remote Sensing ◽

Soil Moisture ◽

Water Temperature ◽

Temperature Data ◽

Data Sets ◽

Surface Water Temperature ◽

Passive Remote Sensing ◽

Water Temperature Data

Inland open water bodies often pose a systematic error source in the passive remote sensing retrievals of soil moisture. Water temperature is a necessary variable used to compute water emissions that is required to be subtracted from satellite observation to yield actual emissions from the land portion, which in turn generates accurate soil moisture retrievals. Therefore, overestimation of soil moisture can often be corrected using concurrent water temperature data in the overall mitigation procedure. In recent years, several data sets of lake water temperature have become available, but their specifications and accuracy have rarely been investigated in the context of passive soil moisture remote sensing on a global scale. For this reason, three lake temperature products were evaluated against in-situ measurements from 2007 to 2011. The data sets include the lake surface water temperature (LSWT) from Global Observatory of Lake Responses to Environmental Change (GloboLakes), the Copernicus Global Land Operations Cryosphere and Water (C-GLOPS), as well as the lake mix-layer temperature (LMLT) from the European Centers for Medium-Range Weather Forecast (ECMWF) ERA5 Land Reanalysis. GloboLakes, C-GLOPS, and ERA5 Land have overall comparable performance with Pearson correlations (R) of 0.87, 0.92 and 0.88 in comparison with in-situ measurements. LSWT products exhibit negative median biases of −0.27 K (GloboLakes) and −0.31 K (C-GLOPS), whereas the median bias of LMLT is 1.56 K. When mapped from their respective native resolutions to a common 9 km Equal-Area Scalable Earth (EASE) Grid 2.0 projection, similar relative performance was observed. LMLT and LSWT data are closer in performance over the 9 km grid cells that exhibit a small range of lake cover fractions (0.05–0.5). Despite comparable relative performance, ERA5 Land shows great advantages in spatial coverage and temporal resolution. In summary, an integrated evaluation on data accuracy, long-term availability, global coverage, temporal resolution, and regular forward processing with modest data latency led us to conclude that LMLT from the ERA5 Land Reanalysis product represents the most optimal path for use in the development of a long-term soil moisture product.

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Global soil moisture data derived through machine learning trained with in-situ measurements

Scientific Data ◽

10.1038/s41597-021-00964-1 ◽

2021 ◽

Vol 8 (1) ◽

Author(s):

Sungmin O. ◽

Rene Orth

Keyword(s):

Machine Learning ◽

Soil Moisture ◽

Large Scale ◽

Short Term Memory ◽

Temporal Dynamics ◽

Soil Moisture Data ◽

Wide Range ◽

Global Soil

AbstractWhile soil moisture information is essential for a wide range of hydrologic and climate applications, spatially-continuous soil moisture data is only available from satellite observations or model simulations. Here we present a global, long-term dataset of soil moisture derived through machine learning trained with in-situ measurements, SoMo.ml. We train a Long Short-Term Memory (LSTM) model to extrapolate daily soil moisture dynamics in space and in time, based on in-situ data collected from more than 1,000 stations across the globe. SoMo.ml provides multi-layer soil moisture data (0–10 cm, 10–30 cm, and 30–50 cm) at 0.25° spatial and daily temporal resolution over the period 2000–2019. The performance of the resulting dataset is evaluated through cross validation and inter-comparison with existing soil moisture datasets. SoMo.ml performs especially well in terms of temporal dynamics, making it particularly useful for applications requiring time-varying soil moisture, such as anomaly detection and memory analyses. SoMo.ml complements the existing suite of modelled and satellite-based datasets given its distinct derivation, to support large-scale hydrological, meteorological, and ecological analyses.

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Evaluating Machine Learning and Geostatistical Methods for Spatial Gap-filling of Monthly ESA CCI Soil Moisture in China

Remote Sensing ◽

10.3390/rs13142848 ◽

2021 ◽

Vol 13 (14) ◽

pp. 2848

Author(s):

Hao Sun ◽

Qian Xu

Keyword(s):

Machine Learning ◽

Soil Moisture ◽

Water Resource Management ◽

Large Scale ◽

Gap Filling ◽

Spatial Continuity ◽

Study Results ◽

Data Gaps

Obtaining large-scale, long-term, and spatial continuous soil moisture (SM) data is crucial for climate change, hydrology, and water resource management, etc. ESA CCI SM is such a large-scale and long-term SM (longer than 40 years until now). However, there exist data gaps, especially for the area of China, due to the limitations in remote sensing of SM such as complex topography, human-induced radio frequency interference (RFI), and vegetation disturbances, etc. The data gaps make the CCI SM data cannot achieve spatial continuity, which entails the study of gap-filling methods. In order to develop suitable methods to fill the gaps of CCI SM in the whole area of China, we compared typical Machine Learning (ML) methods, including Random Forest method (RF), Feedforward Neural Network method (FNN), and Generalized Linear Model (GLM) with a geostatistical method, i.e., Ordinary Kriging (OK) in this study. More than 30 years of passive–active combined CCI SM from 1982 to 2018 and other biophysical variables such as Normalized Difference Vegetation Index (NDVI), precipitation, air temperature, Digital Elevation Model (DEM), soil type, and in situ SM from International Soil Moisture Network (ISMN) were utilized in this study. Results indicated that: 1) the data gap of CCI SM is frequent in China, which is found not only in cold seasons and areas but also in warm seasons and areas. The ratio of gap pixel numbers to the whole pixel numbers can be greater than 80%, and its average is around 40%. 2) ML methods can fill the gaps of CCI SM all up. Among the ML methods, RF had the best performance in fitting the relationship between CCI SM and biophysical variables. 3) Over simulated gap areas, RF had a comparable performance with OK, and they outperformed the FNN and GLM methods greatly. 4) Over in situ SM networks, RF achieved better performance than the OK method. 5) We also explored various strategies for gap-filling CCI SM. Results demonstrated that the strategy of constructing a monthly model with one RF for simulating monthly average SM and another RF for simulating monthly SM disturbance achieved the best performance. Such strategy combining with the ML method such as the RF is suggested in this study for filling the gaps of CCI SM in China.

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The Bolivian Drought Monitor: an operational tool, calibrated on impact records, to identify and communicate drought severity conditions.

10.5194/egusphere-egu21-8480 ◽

2021 ◽

Author(s):

Lauro Rossi ◽

Alessandro Masoero ◽

Anna Mapelli ◽

Fabio Castelli

Keyword(s):

Remote Sensing ◽

Early Warning ◽

Warning System ◽

Drought Severity ◽

Severe Drought ◽

Drought Event ◽

Context Analysis ◽

Management Platform ◽

Drought Conditions ◽

Civil Defence

Within the framework of the CIF financed &#8220;Pilot Program for Climate Resilience&#8221;, the Drought Monitoring and Early Warning System for Bolivia was developed and implemented. The system is operational since July 2020 and aims at detecting emerging severe drought conditions in the country, in order to trigger timely warnings to stakeholders and the general public.The Bolivian Drought Monitor has two main components: a technical one (data gathering and analysis, performed through the multi-hazard early warning &#8220;myDEWETRA&#8221; platform) and an institutional one (creating consensus and disseminating warnings). The system design followed a participatory approach, involving since the early stages the Ministry for Water and Environment (MMAyA), the National Hydrometeorological Service (SENAMHI), the Vice-Ministry for Civil Defence (VIDECI). These institutions actively contribute to the monthly edition of the drought bulletin, each one for its own sector of competence, through a dedicated IT tool for synchronous compilation. Ongoing drought conditions are reported in a national bulletin, issued monthly and published on a dedicated public website: http://monitorsequias.senamhi.gob.bo/Given the Bolivian data-poor context, analysis strongly relies on a large variety of multi-source satellite products, spanning from well consolidated ones in the operational practice to more experimental ones such as from the SMAP mission. This information is used to monthly refresh the spatial maps of 17 indexes covering meteorological, hydrological and agricultural droughts for different aggregation periods (from 1 to 12 months). Simulation of the system performance over a long period (2002-2019) and comparison with recorded socio-economic drought impacts&#160; from the National Disaster Observatory (Observatorio Nacional de Desastres- OND) of the Vice-Ministry of Civil Defence (VIDECI) was used to define a most representative compound index, based on a weighted combination of a selection of 4 indexes with their related thresholds. The combination of 3-month SPEI, 2-month SWDI, 1-month VHI and 1-month FAPAR indexes performed the best in the comparison with impact records. This combination encompasses both the medium-term effects of meteorological and hydrological deficits (3-month SPEI and SWDI), both the short-term effects on vegetation (1-month VHI and FAPAR). This set of indexes proved to be a solid proxy in estimating possible impacts on population of ongoing or incoming drought spells, as happened for most significant recent drought events occurred in Bolivia, such as the 2010 event in the Chaco region and the 2016 drought event in the Altiplano and Valles regions, that heavily affected the water supply in several major cities (La Paz, Sucre, Cochabamba, Oruro and Potos&#237;).The design of the monitoring and bulletin management platform, together with its strong remote-sensing base, give to the system a high potential for easy export to other regional and national contexts. Also, the variety of the different computed drought indexes and the replicability of the procedure for the best compound index identification will allow for efficient evolutionary maintenance as new remote-sensing products will be available in the future.

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Hochaufgelöste Simulation von meteorologischen Feldern in urbanen Räumen mit dem Weather Research and Forecasting (WRF) Modell

10.5194/dach2022-69 ◽

2021 ◽

Author(s):

Lukas N. Pilz ◽

Sanam N. Vardag ◽

Joachim Fallmann ◽

André Butz

Keyword(s):

Greenhouse Gas ◽

Monitoring System ◽

Weather Research And Forecasting ◽

Gas Monitoring ◽

Weather Forecasts ◽

European Centre ◽

Medium Range ◽

Erste Ergebnisse ◽

Weather Research

St&#228;dte und Kommunen sind f&#252;r mehr als 70% der globalen, fossilen CO2-Emissionen verantwortlich, sodass hier ein enormes Mitigationspotential besteht. Informationen &#252;ber (inner-)st&#228;dtische CO2-Emissionen stehen allerdings oft nicht in hoher zeitlicher und r&#228;umlicher Aufl&#246;sung zur Verf&#252;gung und sind meist mit gro&#223;en Unsicherheiten behaftet. Diese Umst&#228;nde erschweren eine zielgerichtete und effiziente Mitigation im urbanen Raum. St&#228;dtische Messnetzwerke k&#246;nnen als unabh&#228;ngige Informationsquelle einen Beitrag leisten, um CO2-Emissionen in St&#228;dten zu quantifizieren und Mitigation zu verifizieren. Verschiedene denkbare Beobachtungsstrategien sollten im Vorfeld abgew&#228;gt werden, um urbane Emissionen bestm&#246;glich, d.h. mit der erforderlichen Genauigkeit und Kosteneffizienz zu quantifizieren. So k&#246;nnen Messnetzwerke die Basis f&#252;r zielgerichtete und kosteneffiziente Mitigation legen.Im Rahmen des Verbundvorhabens &#8222;Integrated Greenhouse Gas Monitoring System for Germany&#8220; (ITMS) werden wir verschiedene Beobachtungsstrategien f&#252;r urbane R&#228;ume entwerfen und mit Hilfe von Modellsimulation evaluieren und abw&#228;gen. Notwendige Voraussetzung f&#252;r die Evaluation der Strategien ist eine akkurate Repr&#228;sentation des atmosph&#228;rischen Transports im Modell.Diese Studie zeigt erste Ergebnisse der hochaufl&#246;senden (1kmx1km) meteorologischen Simulationen f&#252;r den Rhein-Neckar-Raum mit dem WRF Modell. Die in WRF simulierten meteorologischen Gr&#246;&#223;en werden f&#252;r verschiedene Modellkonfigurationen mit re-analysierten Daten des European Centre for Medium-Range Weather Forecasts (ECMWF) und ausgew&#228;hlten Messstationen verglichen. Damit evaluieren wir den Einfluss unterschiedlicher Nudging-Strategien, Parametrisierungen physikalischer Prozesse und urbaner Interaktionen auf die Modellperformance von Lufttemperatur, Windrichtung, Windgeschwindigkeit und Grenzschichth&#246;he. Durch diese Analysen gew&#228;hrleisten wir, dass die Simulation der Beobachtungsstrategien auf robustem und realistischem atmosph&#228;rischen Transport basieren und schlussendlich repr&#228;sentative Empfehlungen f&#252;r den Aufbau von Messnetzwerken liefern k&#246;nnen.

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Evaluierung des nichthydrostatischen mesoskaligen Modells GRAMM-SCI anhand der Richtlinie VDI 3783 Blatt 7/Evaluation of the nonhydrostatic mesoscale model GRAMM-SCI based on the guideline VDI 3783 Part 7

Gefahrstoffe ◽

10.37544/0949-8036-2020-07-08-68 ◽

2020 ◽

Vol 80 (07-08) ◽

pp. 318-324

Author(s):

D. Öttl

Keyword(s):

Mesoscale Model ◽

Weather Forecasts ◽

European Centre ◽

Medium Range

Aufgrund der komplexen Orografie in den Alpen sind einfache, auf diagnostischen Ansätzen beruhende Windfeldmodelle in Österreich kaum anwendbar. Daher wird in den meisten österreichischen Bundesländern das mesoskalige Modell GRAMM im Rahmen von Luftschadstoffuntersuchungen eingesetzt. In diesem Beitrag werden Ergebnisse der Modellevaluierung anhand jener drei Datensätze der Richtlinie VDI 3783 Blatt 7 präsentiert, die auf teils umfangreichen Messkampagnen basieren. Das Modell GRAMM wurde mittlerweile erweitert (Version GRAMM-SCI) und kann nun auch mit den Reanalysedaten ERA5 des Europäischen Wetterdienstes (European Centre for Medium-Range Weather Forecasts, ECMWF) angetrieben werden. Um die Qualität der ERA5-Daten zu prüfen, wurden zusätzliche Simulationen für die drei Evaluierungsdatensätze aus VDI 3783 Blatt 7 durchgeführt. Es zeigt sich, dass Modellsimulationen mit GRAMM-SCI, die auf ERA5-Daten basieren, die Strömungs- und Temperaturverhältnisse grundsätzlich gut wiedergeben. Allerdings sind die Abweichungen zu den Messungen der Sondermesskampagnen teilweise etwas zu groß, um die hohen Anforderungen von VDI 3783 Blatt 7 an die Modellergebnisse vollständig zu erfüllen.

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CARACTERIZAÇÃO ATMOSFÉRICA QUANDO DA OCORRÊNCIA DE EVENTOS EXTREMOS DE CHUVA NA REGIÃO SUL DE MINAS GERAIS

Revista Brasileira de Climatologia ◽

10.5380/abclima.v21i0.47577 ◽

2017 ◽

Vol 21 ◽

Cited By ~ 1

Author(s):

Michelle Simões Reboita ◽

Diogo Malagutti Gonçalves Marietto ◽

Amanda Souza ◽

Marina Barbosa

Keyword(s):

Climate Prediction ◽

Minas Gerais ◽

Weather Forecasts ◽

European Centre ◽

Medium Range ◽

Climate Prediction Center

O objetivo deste estudo é apresentar uma descrição das características da atmosfera que contribuíram para elevados totais de precipitação no sul de Minas Gerais e que foram precursores de dois episódios de inundação e alagamento na cidade de Itajubá: um em 16 de janeiro de 1991 e outro em 02 de janeiro de 2000. Para tanto, foram utilizados dados do Climate Prediction Center e da reanálise ERA-Interim do European Centre for Medium-Range Weather Forecasts (ECMWF). Entre os resultados, têm-se que os episódios de inundação e alagamento ocorridos na cidade de Itajubá, em ambos os anos, estiveram associados à atuação da Zona de Convergência do Atlântico Sul, que se estendia da Amazônia, passando pelo sudeste do Brasil, e chegava ao Atlântico Sul.

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An “island” in the stratosphere – on the enhanced annual variation of water vapour in the middle and upper stratosphere in the southern tropics and subtropics

Atmospheric Chemistry and Physics ◽

10.5194/acp-17-11521-2017 ◽

2017 ◽

Vol 17 (18) ◽

pp. 11521-11539 ◽

Cited By ~ 3

Author(s):

Stefan Lossow ◽

Hella Garny ◽

Patrick Jöckel

Keyword(s):

Water Vapour ◽

Atmospheric Chemistry ◽

Annual Variation ◽

Trace Gases ◽

Michelson Interferometer ◽

Full Account ◽

Weather Forecasts ◽

European Centre ◽

Atmospheric Sounding ◽

Medium Range

Abstract. The amplitude of the annual variation in water vapour exhibits a distinct isolated maximum in the middle and upper stratosphere in the southern tropics and subtropics, peaking typically around 15° S in latitude and close to 3 hPa (∼  40.5 km) in altitude. This enhanced annual variation is primarily related to the Brewer–Dobson circulation and hence also visible in other trace gases. So far this feature has not gained much attention in the literature and the present work aims to add more prominence. Using Envisat/MIPAS (Environmental Satellite/Michelson Interferometer for Passive Atmospheric Sounding) observations and ECHAM/MESSy (European Centre for Medium-Range Weather Forecasts Hamburg/Modular Earth Submodel System) Atmospheric Chemistry (EMAC) simulations we provide a dedicated illustration and a full account of the reasons for this enhanced annual variation.

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Evaluating sub-seasonal heatwave reforecasts of the ECMWF over Europe

10.5194/ems2021-146 ◽

2021 ◽

Author(s):

Natalia Korhonen ◽

Otto Hyvärinen ◽

Matti Kämäräinen ◽

Kirsti Jylhä

Keyword(s):

Heat Wave ◽

Heat Waves ◽

Lead Times ◽

Weather Forecasts ◽

Wave Forecast ◽

Extended Range ◽

Hot Days ◽

Medium Range ◽

Summer Temperatures

Severe heatwaves have harmful impacts on ecosystems and society. Early warning of heat waves help with decreasing their harmful impact. Previous research shows that the Extended Range Forecasts (ERF) of the European Centre for Medium-Range Weather Forecasts (ECMWF) have over Europe a somewhat higher reforecast skill for extreme hot summer temperatures than for long-term mean temperatures. Also it has been shown that the reforecast skill of the ERFs of the ECMWF was strongly increased by the most severe heat waves (the European heatwave 2003 and the Russian heatwave 2010).Our aim is to be able to estimate the skill of a heat wave forecast at the time the forecast is given. For that we investigated the spatial and temporal reforecast skill of the ERFs of the ECMWF to forecast hot days (here defined as a day on which the 5 days running mean surface temperature is above its summer 90th percentile) in the continental Europe in summers 2000-2019. We used the ECMWF 2-meter temperature reforecasts and verified them against the ERA5 reanalysis. The skill of the hot day reforecasts was estimated by the symmetric extremal dependence index (SEDI) which considers both hit rates and false alarm rates of the hot day forecasts. Further, we investigated the skill of the heatwave reforecasts based on at which time steps of the forecast the hot days were forecasted. We found that on the mesoscale (horizontal scale of ~500 km) the ERFs of the ECMWF were most skillful in predicting the life cycle of a heat wave (lasting up to 25 days) about a week before its start and during its course. That is, on the mesoscale those reforecasts, in which hot day(s) were forecasted to occur during the first 7&#8230;11 days, were more skillful on lead times up to 25 days than the rest of the heat wave forecasts. This finding is valuable information, e.g., in the energy and health sectors while preparing for a coming heat wave.The work presented here is part of the research project HEATCLIM (Heat and health in the changing climate) funded by the Academy of Finland.

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