scholarly journals LDAS-Monde Sequential Assimilation of Satellite Derived Observations Applied to the CONtiguous US: An ERA-5 Driven Reanalysis of the Land Surface Variables

2018 ◽  
Author(s):  
Clément Albergel ◽  
Simon Munier ◽  
Aymeric Bocher ◽  
Bertrand Bonan ◽  
Yongjun Zheng ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Initial Conditions ◽  
The United States ◽  
Agricultural Drought ◽  
Reference Network ◽  
Surface Model ◽  
Area Index ◽  
Medium Range Weather Forecast ◽  
The Impact

LDAS-Monde, an offline land data assimilation system with global capacity, is applied over the CONtiguous US (CONUS) domain to enhance monitoring accuracy for water and energy states and fluxes. LDAS-Monde ingests satellite-derived Surface Soil Moisture (SSM) and Leaf Area Index (LAI) estimates to constrain the Interactions between Soil, Biosphere, and Atmosphere (ISBA) Land Surface Model (LSM) coupled with the CNRM (Centre National de Recherches Météorologiques) version of the Total Runoff Integrating Pathways (CTRIP) continental hydrological system (ISBA-CTRIP). LDAS-Monde is forced by the ERA-5 atmospheric reanalysis from the European Center For Medium Range Weather Forecast (ECMWF) from 2010 to 2016 leading to a 7-yr, quarter degree spatial resolution offline reanalysis of Land Surface Variables (LSVs) over CONUS. The impact of assimilating LAI and SSM into LDAS-Monde is assessed over North America, by comparison to satellite-driven model estimates of land evapotranspiration from the Global Land Evaporation Amsterdam Model (GLEAM) project, and upscaled ground-based observations of gross primary productivity from the FLUXCOM project. Also, taking advantage of the relatively dense data networks over CONUS, we also evaluate the impact of the assimilation against in-situ measurements of soil moisture from the USCRN network (US Climate Reference Network) are used in the evaluation, together with river discharges from the United States Geophysical Survey (USGS) and the Global Runoff Data Centre (GRDC). Those data sets highlight the added value of assimilating satellite derived observations compared to an open-loop simulation (i.e. no assimilation). It is shown that LDAS-Monde has the ability not only to monitor land surface variables but also to forecast them, by providing improved initial conditions which impacts persist through time. LDAS-Monde reanalysis has a potential to be used to monitor extreme events like agricultural drought, also. Finally, limitations related to LDAS-Monde and current satellite-derived observations are exposed as well as several insights on how to use alternative datasets to analyze soil moisture and vegetation state.

scholarly journals Monitoring and Forecasting the Impact of the 2018 Summer Heatwave on Vegetation

2019 ◽  
Author(s):  
Clément Albergel ◽  
Emanuel Dutra ◽  
Bertrand Bonan ◽  
Yongjun Zheng ◽  
Simon Munier ◽  
...  
Keyword(s):  
Soil Moisture ◽  
High Resolution ◽  
Land Surface ◽  
Western Europe ◽  
Initial Conditions ◽  
Added Value ◽  
Area Index ◽  
Long Time ◽  
Land Data Assimilation System ◽  
The Impact

This study aims to assess the potential of the LDAS-Monde a land data assimilation system developed by Météo-France to monitor the impact of the 2018 summer heatwave over western Europe vegetation state. The LDAS-Monde is forced by the ECMWF’s (i) ERA5 reanalysis, and (ii) the Integrated Forecasting System High Resolution operational analysis (IFS-HRES), used in conjunction with the assimilation of Copernicus Global Land Service (CGLS) satellite derived products, namely the Surface Soil Moisture (SSM) and the Leaf Area Index (LAI). Analysis of long time series of satellite derived CGLS LAI (2000-2018) and SSM (2008-2018) highlights marked negative anomalies for July 2018 affecting large areas of North Western Europe and reflects the impact of the heatwave. Such large anomalies spreading over a large part of the considered domain have never been observed in the LAI product over this 18-yr period. The LDAS-Monde land surface reanalyses were produced at spatial resolutions of 0.25°x0.25° (January 2008 to October 2018) and 0.10°x0.10° (April 2016 to December 2018). Both configuration of the LDAS-Monde forced by either ERA5 or HRES capture well the vegetation state in general and for this specific event, with HRES configuration exhibiting better monitoring skills than ERA5 configuration. The consistency of ERA5 and IFS HRES driven simulations over the common period (April 2016 to October 2018) allowed to disentangle and appreciate the origin of improvements observed between the ERA5 and HRES. Another experiment, down-scaling ERA5 to HRES spatial resolutions, was performed. Results suggest that land surface spatial resolution is key (e.g. associated to a better representation of the land cover, topography) and using HRES forcing still enhance the skill. While there are advantages in using HRES, there is added value in down-scaling ERA5, which can provide consistent, long term, high resolution land reanalysis. If the improvement from LDAS-Monde analysis on control variables (soil moisture from layers 2 to 8 of the model representing the first meter of soil and LAI) from the assimilation of SSM and LAI was expected, other model variables benefit from the assimilation through biophysical processes and feedbacks in the model. Finally, we also found added value of initializing 8-day land surface HRES driven forecasts from LDAS-Monde analysis when compared with model only initial conditions.

Assimilation of vegetation conditions improves the representation of drought over agricultural areas

2021 ◽  
Author(s):  
David M. Mocko ◽  
Sujay V. Kumar ◽  
Christa D. Peters-Lidard ◽  
Shugong Wang
Keyword(s):  
Land Surface ◽  
Land Surface Model ◽  
Agricultural Drought ◽  
False Alarms ◽  
Surface Model ◽  
Model Errors ◽  
Physical Processes ◽  
Area Index ◽  
Agricultural Areas ◽  
The Impact

AbstractThis study presents an evaluation of the impact of vegetation conditions on a land-surface model (LSM) simulation of agricultural drought. The Noah-MP LSM is used to simulate water and energy fluxes and states, which are transformed into drought categories using percentiles over the continental U.S. from 1979 to 2017. Leaf Area Index (LAI) observations are assimilated into the dynamic vegetation scheme of Noah-MP. A weekly operational drought monitor (the U.S. Drought Monitor) is used for the evaluation. The results show that LAI assimilation into Noah-MP’s dynamic vegetation scheme improves the model's ability to represent drought, particularly over cropland areas. LAI assimilation improves the simulation of the drought category, detection of drought conditions, and reduces the instances of drought false alarms. The assimilation of LAI in these locations not only corrects model errors in the simulation of vegetation, but also can help to represent unmodeled physical processes such as irrigation towards improved simulation of agricultural drought.

scholarly journals On the ability of Sentinel-1 backscatter to detect soil moisture and vegetation changes caused by irrigation fluxes over the Po River Valley (Italy)

2021 ◽  
Author(s):  
Sara Modanesi ◽  
Christian Massari ◽  
Alexander Gruber ◽  
Luca Brocca ◽  
Hans Lievens ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Land Surface ◽  
River Valley ◽  
System Level ◽  
Surface Model ◽  
Irrigation Scheme ◽  
Po River ◽  
Area Index ◽  
The Impact

<p>Worldwide, the amount of water used for agricultural purposes is rising because of an increasing food demand. In this context, the detection and quantification of irrigation is crucial, but the availability of ground observations is limited. Therefore, an increasing number of studies are focusing on the use of models and satellite data to detect and quantify irrigation. For instance, the parameterization of irrigation in large scale Land Surface Models (LSM) is improving, but it is still characterized by simplifying assumptions, such as the lack of dynamic crop information, the extent of irrigated areas, and the mostly unknown timing and amount of irrigation. Remote sensing observations offer an opportunity to fill this gap as they are directly affected by, and hence potentially able to detect, irrigation. Therefore, combining models and satellite information through data assimilation can offer a viable way to quantify the water used for irrigation.</p><p>The aim of this study is to test how well modelled soil moisture and vegetation estimates from the Noah-MP LSM, with or without irrigation parameterization in the NASA Land Information System (LIS), are able to mimic in situ observations or to capture the signal of high-resolution Sentinel-1 backscatter observations in an irrigated area. The experiments were carried out over select sites in the Po river Valley, an important agricultural area in Northern Italy. To prepare for a data assimilation system, Level-1 Sentinel-1 backscatter observations, aggregated and sampled onto the 1 km EASE-v2 grid, were used to calibrate a Water Cloud Model (WCM) using simulated soil moisture and Leaf Area Index estimates. The WCM was calibrated with and without activating an irrigation scheme in Noah-MP. Results demonstrate that the use of the irrigation scheme provides the optimal calibration of the WCM, confirming the ability of Sentinel-1 to track the impact of human activities on the water cycle. Additionally, a first data assimilation experiment demonstrates the potential of Sentinel-1 backscatter observations to correct errors in Land Surface Model (LSM) simulations that are caused by unmodelled or wrongly modelled irrigation.</p>

scholarly journals Interpretation of ASCAT Radar Scatterometer Observations Over Land: A Case Study Over Southwestern France

2019 ◽  
Vol 11 (23) ◽  
pp. 2842 ◽  
Author(s):  
Daniel Shamambo ◽  
Bertrand Bonan ◽  
Jean-Christophe Calvet ◽  
Clément Albergel ◽  
Sebastian Hahn
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Extreme Event ◽  
Cloud Model ◽  
Large Fraction ◽  
Land Surface Model ◽  
Surface Model ◽  
Area Index ◽  
The Difference ◽  
The Impact

This paper investigates to what extent soil moisture and vegetation density information can be extracted from the Advanced Scatterometer (ASCAT) satellite-derived radar backscatter (σ°) in a data assimilation context. The impact of independent estimates of the surface soil moisture (SSM) and leaf area index (LAI) of diverse vegetation types on ASCAT σ° observations is simulated over southwestern France using the water cloud model (WCM). The LAI and SSM variables used by the WCM are derived from satellite observations and from the Interactions between Soil, Biosphere, and Atmosphere (ISBA) land surface model, respectively. They permit the calibration of the four parameters of the WCM describing static soil and vegetation characteristics. A seasonal analysis of the model scores shows that the WCM has shortcomings over karstic areas and wheat croplands. In the studied area, the Klaus windstorm in January 2009 damaged a large fraction of the Landes forest. The ability of the WCM to represent the impact of Klaus and to simulate ASCAT σ° observations in contrasting land-cover conditions is explored. The difference in σ° observations between the forest zone affected by the storm and the bordering agricultural areas presents a marked seasonality before the storm. The difference is small in the springtime (from March to May) and large in the autumn (September to November) and wintertime (December to February). After the storm, hardly any seasonality was observed over four years. This study shows that the WCM is able to simulate this extreme event. It is concluded that the WCM could be used as an observation operator for the assimilation of ASCAT σ° observations into the ISBA land surface model.

scholarly journals Impact of Soil Moisture Assimilation on Land Surface Model Spinup and Coupled Land–Atmosphere Prediction

2016 ◽  
Vol 17 (2) ◽  
pp. 517-540 ◽  
Author(s):  
Joseph A. Santanello ◽  
Sujay V. Kumar ◽  
Christa D. Peters-Lidard ◽  
Patricia M. Lawston
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Initial Conditions ◽  
Weather Prediction ◽  
Wrf Model ◽  
Distribution Functions ◽  
Cumulative Distribution ◽  
Satellite Monitoring ◽  
Surface Model ◽  
The Impact

Abstract Advances in satellite monitoring of the terrestrial water cycle have led to a concerted effort to assimilate soil moisture observations from various platforms into offline land surface models (LSMs). One principal but still open question is that of the ability of land data assimilation (LDA) to improve LSM initial conditions for coupled short-term weather prediction. In this study, the impact of assimilating Advanced Microwave Scanning Radiometer for EOS (AMSR-E) soil moisture retrievals on coupled WRF Model forecasts is examined during the summers of dry (2006) and wet (2007) surface conditions in the southern Great Plains. LDA is carried out using NASA’s Land Information System (LIS) and the Noah LSM through an ensemble Kalman filter (EnKF) approach. The impacts of LDA on the 1) soil moisture and soil temperature initial conditions for WRF, 2) land–atmosphere coupling characteristics, and 3) ambient weather of the coupled LIS–WRF simulations are then assessed. Results show that impacts of soil moisture LDA during the spinup can significantly modify LSM states and fluxes, depending on regime and season. Results also indicate that the use of seasonal cumulative distribution functions (CDFs) is more advantageous compared to the traditional annual CDF bias correction strategies. LDA performs consistently regardless of atmospheric forcing applied, with greater improvements seen when using coarser, global forcing products. Downstream impacts on coupled simulations vary according to the strength of the LDA impact at the initialization, where significant modifications to the soil moisture flux–PBL–ambient weather process chain are observed. Overall, this study demonstrates potential for future, higher-resolution soil moisture assimilation applications in weather and climate research.

scholarly journals LDAS-Monde Sequential Assimilation of Satellite Derived Observations Applied to the Contiguous US: An ERA-5 Driven Reanalysis of the Land Surface Variables

2018 ◽  
Vol 10 (10) ◽  
pp. 1627 ◽  
Author(s):  
Clement Albergel ◽  
Simon Munier ◽  
Aymeric Bocher ◽  
Bertrand Bonan ◽  
Yongjun Zheng ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Land Surface ◽  
The United States ◽  
Agricultural Drought ◽  
Land Data Assimilation ◽  
Land Data Assimilation System ◽  
The Impact ◽  
Data Assimilation System ◽  
Assimilation System

Land data assimilation system (LDAS)-Monde, an offline land data assimilation system with global capacity, is applied over the CONtiguous US (CONUS) domain to enhance monitoring accuracy for water and energy states and fluxes. LDAS-Monde ingests satellite-derived surface soil moisture (SSM) and leaf area index (LAI) estimates to constrain the interactions between soil, biosphere, and atmosphere (ISBA) land surface model (LSM) coupled with the CNRM (Centre National de Recherches Météorologiques) version of the total runoff integrating pathways (CTRIP) continental hydrological system (ISBA-CTRIP). LDAS-Monde is forced by the ERA-5 atmospheric reanalysis from the European Center for Medium Range Weather Forecast (ECMWF) from 2010 to 2016 leading to a seven-year, quarter degree spatial resolution offline reanalysis of land surface variables (LSVs) over CONUS. The impact of assimilating LAI and SSM into LDAS-Monde is assessed over North America, by comparison to satellite-driven model estimates of land evapotranspiration from the Global Land Evaporation Amsterdam Model (GLEAM) project, and upscaled ground-based observations of gross primary productivity from the FLUXCOM project. Taking advantage of the relatively dense data networks over CONUS, we have also evaluated the impact of the assimilation against in situ measurements of soil moisture from the USCRN (US Climate Reference Network), together with river discharges from the United States Geological Survey (USGS) and the Global Runoff Data Centre (GRDC). Those data sets highlight the added value of assimilating satellite derived observations compared with an open-loop simulation (i.e., no assimilation). It is shown that LDAS-Monde has the ability not only to monitor land surface variables but also to forecast them, by providing improved initial conditions, which impacts persist through time. LDAS-Monde reanalysis also has the potential to be used to monitor extreme events like agricultural drought. Finally, limitations related to LDAS-Monde and current satellite-derived observations are exposed as well as several insights on how to use alternative datasets to analyze soil moisture and vegetation state.

scholarly journals Monitoring and Forecasting the Impact of the 2018 Summer Heatwave on Vegetation

2019 ◽  
Vol 11 (5) ◽  
pp. 520 ◽  
Author(s):  
Clément Albergel ◽  
Emanuel Dutra ◽  
Bertrand Bonan ◽  
Yongjun Zheng ◽  
Simon Munier ◽  
...  
Keyword(s):  
Soil Moisture ◽  
High Resolution ◽  
Land Surface ◽  
Western Europe ◽  
Initial Conditions ◽  
Added Value ◽  
Area Index ◽  
Long Time ◽  
Land Data Assimilation System ◽  
The Impact

This study aims to assess the potential of the LDAS-Monde platform, a land data assimilation system developed by Météo-France, to monitor the impact on vegetation state of the 2018 summer heatwave over Western Europe. The LDAS-Monde is driven by ECMWF’s (i) ERA5 reanalysis, and (ii) the Integrated Forecasting System High Resolution operational analysis (IFS-HRES), used in conjunction with the assimilation of Copernicus Global Land Service (CGLS) satellite-derived products, namely the Surface Soil Moisture (SSM) and the Leaf Area Index (LAI). The study of long time series of satellite derived CGLS LAI (2000–2018) and SSM (2008–2018) highlights marked negative anomalies for July 2018 affecting large areas of northwestern Europe and reflects the impact of the heatwave. Such large anomalies spreading over a large part of the domain of interest have never been observed in the LAI product over this 19-year period. LDAS-Monde land surface reanalyses were produced at spatial resolutions of 0.25° × 0.25° (January 2008 to October 2018) and 0.10° × 0.10° (April 2016 to December 2018). Both configurations of LDAS-Monde forced by either ERA5 or HRES capture well the vegetation state in general and for this specific event, with HRES configuration exhibiting better monitoring skills than ERA5 configuration. The consistency of ERA5- and IFS HRES-driven simulations over the common period (April 2016 to October 2018) allowed to disentangle and appreciate the origin of improvements observed between the ERA5 and HRES. Another experiment, down-scaling ERA5 to HRES spatial resolutions, was performed. Results suggest that land surface spatial resolution is key (e.g., associated to a better representation of the land cover, topography) and using HRES forcing still enhances the skill. While there are advantages in using HRES, there is added value in down-scaling ERA5, which can provide consistent, long term, high resolution land reanalysis. If the improvement from LDAS-Monde analysis on control variables (soil moisture from layers 2 to 8 of the model representing the first meter of soil and LAI) from the assimilation of SSM and LAI was expected, other model variables benefit from the assimilation through biophysical processes and feedback in the model. Finally, we also found added value of initializing 8-day land surface HRES driven forecasts from LDAS-Monde analysis when compared with model-only initial conditions.

scholarly journals Influence of Land Cover and Soil Moisture on the Horizontal Distribution of Sensible and Latent Heat Fluxes in Southeast Kansas during IHOP_2002 and CASES-97

2007 ◽  
Vol 8 (1) ◽  
pp. 68-87 ◽  
Author(s):  
Margaret A. LeMone ◽  
Fei Chen ◽  
Joseph G. Alfieri ◽  
Mukul Tewari ◽  
Bart Geerts ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Latent Heat ◽  
Land Surface ◽  
Field Experiments ◽  
Time Of Day ◽  
Horizontal Distribution ◽  
Heat Fluxes ◽  
Surface Model ◽  
Green Vegetation ◽  
The Impact

Abstract Analyses of daytime fair-weather aircraft and surface-flux tower data from the May–June 2002 International H2O Project (IHOP_2002) and the April–May 1997 Cooperative Atmosphere Surface Exchange Study (CASES-97) are used to document the role of vegetation, soil moisture, and terrain in determining the horizontal variability of latent heat LE and sensible heat H along a 46-km flight track in southeast Kansas. Combining the two field experiments clearly reveals the strong influence of vegetation cover, with H maxima over sparse/dormant vegetation, and H minima over green vegetation; and, to a lesser extent, LE maxima over green vegetation, and LE minima over sparse/dormant vegetation. If the small number of cases is producing the correct trend, other effects of vegetation and the impact of soil moisture emerge through examining the slope ΔxyLE/ΔxyH for the best-fit straight line for plots of time-averaged LE as a function of time-averaged H over the area. Based on the surface energy balance, H + LE = Rnet − Gsfc, where Rnet is the net radiation and Gsfc is the flux into the soil; Rnet − Gsfc ∼ constant over the area implies an approximately −1 slope. Right after rainfall, H and LE vary too little horizontally to define a slope. After sufficient drying to produce enough horizontal variation to define a slope, a steep (∼−2) slope emerges. The slope becomes shallower and better defined with time as H and LE horizontal variability increases. Similarly, the slope becomes more negative with moister soils. In addition, the slope can change with time of day due to phase differences in H and LE. These trends are based on land surface model (LSM) runs and observations collected under nearly clear skies; the vegetation is unstressed for the days examined. LSM runs suggest terrain may also play a role, but observational support is weak.

scholarly journals Sequential assimilation of satellite-derived vegetation and soil moisture products using SURFEX_v8.0: LDAS-Monde assessment over the Euro-Mediterranean area

2017 ◽  
Author(s):  
Clément Albergel ◽  
Simon Munier ◽  
Delphine Jennifer Leroux ◽  
Hélène Dewaele ◽  
David Fairbairn ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Gross Primary Production ◽  
Mediterranean Area ◽  
Surface Model ◽  
Strong Impact ◽  
Control Variables ◽  
Area Index ◽  
Model Control ◽  
Global Land

Abstract. In this study, a global Land Data Assimilation system (LDAS-Monde) is tested over Europe and the Mediterranean basin to increase monitoring accuracy for land surface variables. LDAS-Monde is able to ingest information from satellite-derived surface Soil Moisture (SM) and Leaf Area Index (LAI) observations to constrain the Interactions between Soil, Biosphere, and Atmosphere (ISBA) land surface model (LSM) coupled with the CNRM (Centre National de Recherches Météorologiques) version of the Total Runoff Integrating Pathways (ISBA-CTRIP) continental hydrological system. It makes use of the CO2-responsive version of ISBA which models leaf-scale physiological processes and plant growth. Transfer of water and heat in the soil rely on a multilayer diffusion scheme. Surface SM and LAI observations are assimilated using a simplified extended Kalman filter (SEKF), which uses finite differences from perturbed simulations to generate flow-dependence between the observations and the model control variables. The latter include LAI and seven layers of soil (from 1 cm to 100 cm depth). A sensitivity test of the Jacobians over 2000–2012 exhibits effects related to both depth and season. It also suggests that observations of both LAI and surface SM have an impact on the different control variables. From the assimilation of surface SM, the LDAS is more effective in modifying soil-moisture from the top layers of soil as model sensitivity to surface SM decreases with depth and has almost no impact from 60 cm downwards. From the assimilation of LAI, a strong impact on LAI itself is found. The LAI assimilation impact is more pronounced in SM layers that contain the highest fraction of roots (from 10 cm to 60 cm). The assimilation is more efficient in summer and autumn than in winter and spring. Assimilation impact shows that the LDAS works well constraining the model to the observations and that stronger corrections are applied to LAI than to SM. The assimilation impact's evaluation is successfully carried out using (i) agricultural statistics over France, (ii) river discharge observations, (iii) satellite-derived estimates of land evapotranspiration from the Global Land Evaporation Amsterdam Model (GLEAM) project and (iv) spatially gridded observations based estimates of up-scaled gross primary production and evapotranspiration from the FLUXNET network. Comparisons with those four datasets highlight neutral to highly positive improvement.

scholarly journals SMOS Neural Network Soil Moisture Data Assimilation in a Land Surface Model and Atmospheric Impact

2019 ◽  
Author(s):  
Nemesio Rodriguez-Fernandez ◽  
Patricia de Rosnay ◽  
Clement Albergel ◽  
Philippe Richaume ◽  
Filipe Aires ◽  
...  
Keyword(s):  
Neural Network ◽  
Soil Moisture ◽  
Data Assimilation ◽  
Land Surface ◽  
Positive Impact ◽  
Land Surface Model ◽  
Surface Model ◽  
Northern Asia ◽  
The Impact

The assimilation of Soil Moisture and Ocean Salinity (SMOS) data into the ECMWF (European Centre for Medium Range Weather Forecasts) H-TESSEL (Hydrology revised - Tiled ECMWF Scheme for Surface Exchanges over Land) model is presented. SMOS soil moisture (SM) estimates have been produced specifically by training a neural network with SMOS brightness temperatures as input and H-TESSEL model SM simulations as reference. This can help the assimilation of SMOS information in several ways: (1) the neural network soil moisture (NNSM) data have a similar climatology to the model, (2) no global bias is present with respect to the model even if regional differences can exist. Experiments performing joint data assimilation (DA) of NNSM, 2 metre air temperature and relative humidity or NNSM-only DA are discussed. The resulting SM was evaluated against a large number of in situ measurements of SM obtaining similar results to those of the model with no assimilation, even if significant differences were found from site to site. In addition, atmospheric forecasts initialized with H-TESSEL runs (without DA) or with the analysed SM were compared to measure of the impact of the satellite information. Although, NNSM DA has an overall neutral impact in the forecast in the Tropics, a significant positive impact was found in other areas and periods, especially in regions with limited in situ information. The joint NNSM, T2m and RH2m DA improves the forecast for all the seasons in the Southern Hemisphere. The impact is mostly due to T2m and RH2m, but SMOS NN DA alone also improves the forecast in July- September. In the Northern Hemisphere, the joint NNSM, T2m and RH2m DA improves the forecast in April-September, while NNSM alone has a significant positive effect in July-September. Furthermore, forecasting skill maps show that SMOS NNSM improves the forecast in North America and in Northern Asia for up to 72 hours lead time.

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