Examining the Impact of SMAP Soil Moisture Retrievals on Short-Range Weather Prediction under Weakly and Strongly Coupled Data Assimilation with WRF-Noah

2019 ◽  
Vol 147 (12) ◽  
pp. 4345-4366 ◽  
Author(s):  
Liao-Fan Lin ◽  
Zhaoxia Pu
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Land Surface ◽  
Weather Prediction ◽  
Surface Model ◽  
Strongly Coupled ◽  
Soil Moisture Data ◽  
Analysis Scheme ◽  
Coupled Data Assimilation ◽  
The Impact

Abstract Remotely sensed soil moisture data are typically incorporated into numerical weather models under a framework of weakly coupled data assimilation (WCDA), with a land surface analysis scheme independent from the atmospheric analysis component. In contrast, strongly coupled data assimilation (SCDA) allows simultaneous correction of atmospheric and land surface states but has not been sufficiently explored with land surface soil moisture data assimilation. This study implemented a variational approach to assimilate the Soil Moisture Active Passive (SMAP) 9-km enhanced retrievals into the Noah land surface model coupled with the Weather Research and Forecasting (WRF) Model under a framework of both WCDA and SCDA. The goal of the study is to quantify the relative impact of assimilating SMAP data under different coupling frameworks on the atmospheric forecasts in the summer. The results of the numerical experiments during July 2016 show that SCDA can provide additional benefits on the forecasts of air temperature and humidity compared to WCDA. Over the U.S. Great Plains, assimilation of SMAP data under WCDA reduces a warm bias in temperature and a dry bias in humidity by 7.3% and 19.3%, respectively, while the SCDA case contributes an additional bias reduction of 2.2% (temperature) and 3.3% (humidity). While WCDA leads to a reduction of RMSE in temperature forecasts by 4.1%, SCDA results in additional reduction of RMSE by 0.8%. For the humidity, the reduction of RMSE is around 1% for both WCDA and SCDA.

Analysis of Soil Moisture Changes in Europe during a Single Growing Season in a New ECMWF Soil Moisture Assimilation System

2008 ◽  
Vol 9 (1) ◽  
pp. 116-131 ◽  
Author(s):  
Bart van den Hurk ◽  
Janneke Ettema ◽  
Pedro Viterbo
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Land Surface ◽  
Land Surface Model ◽  
Growing Season ◽  
Atmospheric Model ◽  
Surface Model ◽  
Soil Moisture Data ◽  
The Impact ◽  
Assimilation System

Abstract This study aims at stimulating the development of soil moisture data assimilation systems in a direction where they can provide both the necessary control of slow drift in operational NWP applications and support the physical insight in the performance of the land surface component. It addresses four topics concerning the systematic nature of soil moisture data assimilation experiments over Europe during the growing season of 2000 involving the European Centre for Medium-Range Weather Forecasts (ECMWF) model infrastructure. In the first topic the effect of the (spinup related) bias in 40-yr ECMWF Re-Analysis (ERA-40) precipitation on the data assimilation is analyzed. From results averaged over 36 European locations, it appears that about half of the soil moisture increments in the 2000 growing season are attributable to the precipitation bias. A second topic considers a new soil moisture data assimilation system, demonstrated in a coupled single-column model (SCM) setup, where precipitation and radiation are derived from observations instead of from atmospheric model fields. For many of the considered locations in this new system, the accumulated soil moisture increments still exceed the interannual variability estimated from a multiyear offline land surface model run. A third topic examines the soil water budget in response to these systematic increments. For a number of Mediterranean locations the increments successfully increase the surface evaporation, as is expected from the fact that atmospheric moisture deficit information is the key driver of soil moisture adjustment. In many other locations, however, evaporation is constrained by the experimental SCM setup and is hardly affected by the data assimilation. Instead, a major portion of the increments eventually leave the soil as runoff. In the fourth topic observed evaporation is used to evaluate the impact of the data assimilation on the forecast quality. In most cases, the difference between the control and data assimilation runs is considerably smaller than the (positive) difference between any of the simulations and the observations.

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.

scholarly journals Combined Assimilation of Satellite Precipitation and Soil Moisture: A Case Study Using TRMM and SMOS Data

2017 ◽  
Vol 145 (12) ◽  
pp. 4997-5014 ◽  
Author(s):  
Liao-Fan Lin ◽  
Ardeshir M. Ebtehaj ◽  
Alejandro N. Flores ◽  
Satish Bastola ◽  
Rafael L. Bras
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Land Surface ◽  
Specific Humidity ◽  
Surface Model ◽  
Precipitation Data ◽  
Near Surface ◽  
Satellite Precipitation ◽  
Soil Moisture Data ◽  
Mean Square Errors

This paper presents a framework that enables simultaneous assimilation of satellite precipitation and soil moisture observations into the coupled Weather Research and Forecasting (WRF) and Noah land surface model through variational approaches. The authors tested the framework by assimilating precipitation data from the Tropical Rainfall Measuring Mission (TRMM) and soil moisture data from the Soil Moisture Ocean Salinity (SMOS) satellite. The results show that assimilation of both TRMM and SMOS data can effectively improve the forecast skills of precipitation, top 10-cm soil moisture, and 2-m temperature and specific humidity. Within a 2-day time window, impacts of precipitation data assimilation on the forecasts remain relatively constant for forecast lead times greater than 6 h, while the influence of soil moisture data assimilation increases with lead time. The study also demonstrates that the forecast skill of precipitation, soil moisture, and near-surface temperature and humidity are further improved when both the TRMM and SMOS data are assimilated. In particular, the combined data assimilation reduces the prediction biases and root-mean-square errors, respectively, by 57% and 6% (for precipitation); 73% and 27% (for soil moisture); 17% and 9% (for 2-m temperature); and 33% and 11% (for 2-m specific humidity).

scholarly journals Streamflow data assimilation for soil moisture analysis

2010 ◽  
Vol 3 (1) ◽  
pp. 1-12 ◽  
Author(s):  
K. Warrach-Sagi ◽  
V. Wulfmeyer
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Land Surface ◽  
Climate Models ◽  
Root Zone ◽  
Weather Forecast ◽  
Surface Model ◽  
River Catchment ◽  
Streamflow Data ◽  
The Impact

Abstract. Streamflow depends on the soil moisture of a river catchment and can be measured with relatively high accuracy. The soil moisture in the root zone influences the latent heat flux and, hence, the quantity and spatial distribution of atmospheric water vapour and precipitation. As numerical weather forecast and climate models require a proper soil moisture initialization for their land surface models, we enhanced an Ensemble Kalman Filter to assimilate streamflow time series into the multi-layer land surface model TERRA-ML of the regional weather forecast model COSMO. The impact of streamflow assimilation was studied by an observing system simulation experiment in the Enz River catchment (located at the downwind side of the northern Black Forest in Germany). The results demonstrate a clear improvement of the soil moisture field in the catchment. We illustrate the potential of streamflow data assimilation for weather forecasting and discuss its spatial and temporal requirements for a corresponding, automated river gauging network.

Parametrizating Soil Surface Fluxes in the JULES Land Surface Model

2021 ◽  
Author(s):  
John Edwards
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Heat Waves ◽  
Numerical Models ◽  
Weather Prediction ◽  
Land Surface Model ◽  
Soil Surface ◽  
Current Status ◽  
Surface Model ◽  
The Impact

<p>The parametrization of land-atmosphere interactions in numerical weather prediction and climate models is a topic of active and growing interest, especially in connection with extreme events such as heat waves and droughts. Semiarid regions are sensitive to drought and are currently expanding, but they are often poorly represented in numerical models. On forecasting timescales, comparisons of simulated land surface temperature against retrievals from satellites often show significant cold biases around noon, whilst, on climate timescales, land surface models often fail to represent droughts realistically. Inadequate treatment of the land surface, and particularly of soil properties and soil moisture, is likely to contribute to such errors.</p> <p>Efforts to develop improved parametrizations of soil processes in the JULES land surface model for application in weather prediction and climate simulations are underway. Whilst processes at the soil surface are a central part of this, to obtain acceptable performance it is also important to consider the surface flux budget as a whole, including the treatment of the plant canopy. Here, we shall describe the current status of developments aimed at improving the representation of evapotranspiration and ground heat fluxes in the model, noting the major issues encountered. The importance of accurately representing the impact of soil moisture on thermal properties will be stressed. Results from initial studies will be presented and we shall offer a perspective on future developments.<br /><br /></p>

Evaluation of a NOAH-MP Land Surface Model Ensemble over the Mississippi Basin

2020 ◽  
Author(s):  
Jonas Rothermel ◽  
Maike Schumacher
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Numerical Weather Prediction ◽  
Land Surface ◽  
Hydrological Cycle ◽  
Weather Prediction ◽  
Surface Model ◽  
Empirical Parameter ◽  
Numerical Weather ◽  
Parameter Values

<p><span>Physical-based Land Surface Models (LSMs) have deepened the understanding of the hydrological cycle and serve as the lower boundary layer in atmospheric models for numerical weather prediction. As any numerical model, they are subject to various sources of uncertainty, including simplified model physics, unknown empirical parameter values and forcing errors, particularly precipitation. Quantifying these uncertainties is important for assessing the predictive power of the model, especially in applications for environmental hazard warning. Data assimilation systems also benefit from realistic model error estimates.</span></p><p><span><span>In this study, the LSM NOAH-MP is evaluated over the Mississippi basin by running a large ensemble of model configurations with suitably perturbed forcing data and parameter values. For this, sensible parameter distributions are obtained by performing a thorough sensitivity analysis, identifying the most informative parameters beforehand by a screening approach. The ensemble of model outputs is compared against various hydrologic and atmospheric feedback observations, including SCAN soil moisture data, GRACE TWS anomaly data and AmeriFlux evapotranspiration measurements. The long-term aim of this study is to improve land-surface states via data assimilation and to investigate their influence on short- to midterm numerical weather prediction. Thus, the uncertainty of the simulated model states, such as snow, soil moisture in various layers, and groundwater are thoroughly studied to estimate the relative impact of possible hydrologic data sets in the assimilation.</span></span></p>

scholarly journals The impact of model and rainfall forcing errors on characterizing soil moisture uncertainty in land surface modeling

2012 ◽  
Vol 16 (10) ◽  
pp. 3499-3515 ◽  
Author(s):  
V. Maggioni ◽  
E. N. Anagnostou ◽  
R. H. Reichle
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Parameter Uncertainty ◽  
Land Surface ◽  
Accurate Estimation ◽  
Surface Model ◽  
Model Parameters ◽  
Model Parameter ◽  
Parameter Perturbations ◽  
The Impact

Abstract. The contribution of rainfall forcing errors relative to model (structural and parameter) uncertainty in the prediction of soil moisture is investigated by integrating the NASA Catchment Land Surface Model (CLSM), forced with hydro-meteorological data, in the Oklahoma region. Rainfall-forcing uncertainty is introduced using a stochastic error model that generates ensemble rainfall fields from satellite rainfall products. The ensemble satellite rain fields are propagated through CLSM to produce soil moisture ensembles. Errors in CLSM are modeled with two different approaches: either by perturbing model parameters (representing model parameter uncertainty) or by adding randomly generated noise (representing model structure and parameter uncertainty) to the model prognostic variables. Our findings highlight that the method currently used in the NASA GEOS-5 Land Data Assimilation System to perturb CLSM variables poorly describes the uncertainty in the predicted soil moisture, even when combined with rainfall model perturbations. On the other hand, by adding model parameter perturbations to rainfall forcing perturbations, a better characterization of uncertainty in soil moisture simulations is observed. Specifically, an analysis of the rank histograms shows that the most consistent ensemble of soil moisture is obtained by combining rainfall and model parameter perturbations. When rainfall forcing and model prognostic perturbations are added, the rank histogram shows a U-shape at the domain average scale, which corresponds to a lack of variability in the forecast ensemble. The more accurate estimation of the soil moisture prediction uncertainty obtained by combining rainfall and parameter perturbations is encouraging for the application of this approach in ensemble data assimilation systems.

Correction of Forcing-Related Spatial Artifacts in a Land Surface Model by Satellite Soil Moisture Data Assimilation

2018 ◽  
Vol 15 (4) ◽  
pp. 498-502 ◽  
Author(s):  
Clay B. Blankenship ◽  
Jonathan L. Case ◽  
William L. Crosson ◽  
Bradley T. Zavodsky
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Land Surface ◽  
Land Surface Model ◽  
Surface Model ◽  
Soil Moisture Data

Effect of Land–Atmosphere Interactions on the IHOP 24–25 May 2002 Convection Case

2006 ◽  
Vol 134 (1) ◽  
pp. 113-133 ◽  
Author(s):  
Teddy R. Holt ◽  
Dev Niyogi ◽  
Fei Chen ◽  
Kevin Manning ◽  
Margaret A. LeMone ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Air Temperature ◽  
Land Surface ◽  
Weather Forecasting ◽  
Wrf Model ◽  
Land Surface Model ◽  
Atmospheric Model ◽  
Surface Model ◽  
The Impact

Abstract Numerical simulations are conducted using the Coupled Ocean/Atmosphere Mesoscale Prediction System (COAMPS) to investigate the impact of land–vegetation processes on the prediction of mesoscale convection observed on 24–25 May 2002 during the International H2O Project (IHOP_2002). The control COAMPS configuration uses the Weather Research and Forecasting (WRF) model version of the Noah land surface model (LSM) initialized using a high-resolution land surface data assimilation system (HRLDAS). Physically consistent surface fields are ensured by an 18-month spinup time for HRLDAS, and physically consistent mesoscale fields are ensured by a 2-day data assimilation spinup for COAMPS. Sensitivity simulations are performed to assess the impact of land–vegetative processes by 1) replacing the Noah LSM with a simple slab soil model (SLAB), 2) adding a photosynthesis, canopy resistance/transpiration scheme [the gas exchange/photosynthesis-based evapotranspiration model (GEM)] to the Noah LSM, and 3) replacing the HRLDAS soil moisture with the National Centers for Environmental Prediction (NCEP) 40-km Eta Data Assimilation (EDAS) operational soil fields. CONTROL, EDAS, and GEM develop convection along the dryline and frontal boundaries 2–3 h after observed, with synoptic-scale forcing determining the location and timing. SLAB convection along the boundaries is further delayed, indicating that detailed surface parameterization is necessary for a realistic model forecast. EDAS soils are generally drier and warmer than HRLDAS, resulting in more extensive development of convection along the dryline than for CONTROL. The inclusion of photosynthesis-based evapotranspiration (GEM) improves predictive skill for both air temperature and moisture. Biases in soil moisture and temperature (as well as air temperature and moisture during the prefrontal period) are larger for EDAS than HRLDAS, indicating land–vegetative processes in EDAS are forced by anomalously warmer and drier conditions than observed. Of the four simulations, the errors in SLAB predictions of these quantities are generally the largest. By adding a sophisticated transpiration model, the atmospheric model is able to better respond to the more detailed representation of soil moisture and temperature. The sensitivity of the synoptically forced convection to soil and vegetative processes including transpiration indicates that detailed representation of land surface processes should be included in weather forecasting models, particularly for severe storm forecasting where local-scale information is important.

scholarly journals Streamflow data assimilation for soil moisture analysis

2009 ◽  
Vol 2 (1) ◽  
pp. 551-579 ◽  
Author(s):  
K. Warrach-Sagi ◽  
V. Wulfmeyer
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Land Surface ◽  
Climate Models ◽  
Root Zone ◽  
Weather Forecast ◽  
Surface Model ◽  
River Catchment ◽  
Streamflow Data ◽  
The Impact

Abstract. Streamflow depends on the soil moisture of a river catchment and can be measured with relatively high accuracy. The soil moisture in the root zone influences the latent heat flux and hence the quantity and spatial distribution of atmospheric water vapour and precipitation. As numerical weather forecast and climate models require a proper soil moisture initialization for their land surface models, we enhanced an Ensemble Kalman Filter to assimilate streamflow timeseries into the multi-layer land surface model TERRA-ML of the regional weather forecast model COSMO. The impact of streamflow assimilation was studied by an observing system simulation experiment in the Enz River catchment (located at the downwind side of the northern Black Forest in Germany). The results demonstrate a clear improvement of the soil moisture field in the catchment. We illustrate the potential of streamflow data assimilation for weather forecasting and discuss its spatial and temporal requirements for a corresponding, automated river gauging network.

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