scholarly journals Satellite soil moisture data assimilation impacts on modeling weather variables and ozone in the southeastern US – Part 1: An overview

2021 ◽  
Vol 21 (14) ◽  
pp. 11013-11040
Author(s):  
Min Huang ◽  
James H. Crawford ◽  
Joshua P. DiGangi ◽  
Gregory R. Carmichael ◽  
Kevin W. Bowman ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Dry Deposition ◽  
Model Performance ◽  
Spatiotemporal Variability ◽  
Anthropogenic Emissions ◽  
Surface Model ◽  
Southeastern Us ◽  
Tropospheric O3 ◽  
Surface O3

Abstract. This study evaluates the impact of satellite soil moisture (SM) data assimilation (DA) on regional weather and ozone (O3) modeling over the southeastern US during the summer. Satellite SM data are assimilated into the Noah land surface model using an ensemble Kalman filter approach within National Aeronautics and Space Administration's Land Information System framework, which is semicoupled with the Weather Research and Forecasting model with online Chemistry (WRF-Chem; standard version 3.9.1.1). The DA impacts on the model performance of SM, weather states, and energy fluxes show strong spatiotemporal variability. Dense vegetation and water use from human activities unaccounted for in the modeling system are among the factors impacting the effectiveness of the DA. The daytime surface O3 responses to the DA can largely be explained by the temperature-driven changes in biogenic emissions of volatile organic compounds and soil nitric oxide, chemical reaction rates, and dry deposition velocities. On a near-biweekly timescale, the DA modified the mean daytime and daily maximum 8 h average surface O3 by up to 2–3 ppbv, with the maximum impacts occurring in areas where daytime surface air temperature most strongly (i.e., by ∼2 K) responded to the DA. The DA impacted WRF-Chem upper tropospheric O3 (e.g., for its daytime-mean, by up to 1–1.5 ppbv) partially via altering the transport of O3 and its precursors from other places as well as in situ chemical production of O3 from lightning and other emissions. Case studies during airborne field campaigns suggest that the DA improved the model treatment of convective transport and/or lightning production. In the cases that the DA improved the modeled SM, weather fields, and some O3-related processes, its influences on the model's O3 performance at various altitudes are not always as desirable. This is in part due to the uncertainty in the model's key chemical inputs, such as anthropogenic emissions, and the model representation of stratosphere–troposphere exchanges. This can also be attributable to shortcomings in model parameterizations (e.g., chemical mechanism, natural emission, photolysis and deposition schemes), including those related to representing water availability impacts. This study also shows that the WRF-Chem upper tropospheric O3 response to the DA has comparable magnitudes with its response to the estimated US anthropogenic emission changes within 2 years. As reductions in anthropogenic emissions in North America would benefit the mitigation of O3 pollution in its downwind regions, this analysis highlights the important role of SM in quantifying air pollutants' source–receptor relationships between the US and its downwind areas. It also emphasizes that using up-to-date anthropogenic emissions is necessary for accurately assessing the DA impacts on the model performance of O3 and other pollutants over a broad region. This work will be followed by a Noah-Multiparameterization (with dynamic vegetation)-based study over the southeastern US, in which selected processes including photosynthesis and O3 dry deposition will be the foci.

scholarly journals Satellite soil moisture data assimilation impacts on modeling weather and ozone in the southeastern US – part I: an overview

2020 ◽  
Author(s):  
Min Huang ◽  
James H. Crawford ◽  
Joshua P. DiGangi ◽  
Gregory R. Carmichael ◽  
Kevin W. Bowman ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Dry Deposition ◽  
Spatiotemporal Variability ◽  
Anthropogenic Emissions ◽  
Surface Model ◽  
Soil Moisture Data ◽  
Southeastern Us ◽  
Tropospheric O3 ◽  
The Impact

Abstract. This study evaluates the impact of satellite soil moisture data assimilation (SM DA) on regional weather and ozone (O3) modeling over the southeastern US during the summer. Satellite SM data are assimilated into the Noah land surface model using an ensemble Kalman filter approach within National Aeronautics and Space Administration's Land Information System framework, which is semicoupled with the Weather Research and Forecasting model with online Chemistry (WRF‐Chem, standard version 3.9.1.1). The SM DA impacts on WRF-Chem performance of weather states and energy fluxes show strong spatiotemporal variability, and many factors such as dense vegetation, complex terrain, and unmodeled water use from human activities may have impacted the effectiveness of the SM DA. The changes in WRF-Chem weather fields due to the SM DA modified various model processes critical to its surface O3 fields, such as biogenic isoprene and soil nitric oxide emissions, photochemical reactions, as well as dry deposition. The SM DA impacted WRF-Chem upper tropospheric O3 partially via altering atmospheric transport and in-situ chemical production of O3 from lightning and other emissions. It is shown that WRF-Chem upper tropospheric O3 response to the SM DA has comparable magnitudes with its response to the estimated US anthropogenic emission changes within two years. As reductions in US anthropogenic emissions would be beneficial for mitigating European O3 pollution, our analysis highlights the important role of SM in quantifying pollutants' transport from the US to Europe. It also emphasizes that using up-to-date anthropogenic emissions is necessary for accurately assessing the SM DA impacts on the model performance of O3 and other pollutants over a broad region. Additionally, this work demonstrates that the SM DA impact on WRF-Chem O3 performance at various altitudes is complicated by not only the model's emission input but also other factors such as the model representation of stratosphere-troposphere exchanges. This work will be followed by a Noah-Multiparameterization (with dynamic vegetation) based study over the southeastern US, in which selected processes including photosynthesis and O3 dry deposition will be the foci.

scholarly journals Using data assimilation to optimize pedotransfer functions using large-scale in-situ soil moisture observations

2020 ◽  
Author(s):  
Elizabeth Cooper ◽  
Eleanor Blyth ◽  
Hollie Cooper ◽  
Rich Ellis ◽  
Ewan Pinnington ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Land Surface ◽  
Large Scale ◽  
Pedotransfer Functions ◽  
Surface Model ◽  
Climate Projections ◽  
Land Surface Models ◽  
Surface Models

Abstract. Soil moisture predictions from land surface models are important in hydrological, ecological and meteorological applications. In recent years the availability of wide-area soil-moisture measurements has increased, but few studies have combined model-based soil moisture predictions with in-situ observations beyond the point scale. Here we show that we can markedly improve soil moisture estimates from the JULES land surface model using field scale observations and data assimilation techniques. Rather than directly updating soil moisture estimates towards observed values, we optimize constants in the underlying pedotransfer functions, which relate soil texture to JULES soil physics parameters. In this way we generate a single set of newly calibrated pedotransfer functions based on observations from a number of UK sites with different soil textures. We demonstrate that calibrating a pedotransfer function in this way can improve the performance of land surface models, leading to the potential for better flood, drought and climate projections.

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 The impact of near-surface soil moisture assimilation at subseasonal, seasonal, and inter-annual timescales

2015 ◽  
Vol 19 (12) ◽  
pp. 4831-4844 ◽  
Author(s):  
C. Draper ◽  
R. Reichle
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Surface Soil ◽  
Root Zone ◽  
Surface Model ◽  
Surface Soil Moisture ◽  
Near Surface ◽  
In Situ Observations ◽  
The Impact

Abstract. A 9 year record of Advanced Microwave Scanning Radiometer – Earth Observing System (AMSR-E) soil moisture retrievals are assimilated into the Catchment land surface model at four locations in the US. The assimilation is evaluated using the unbiased mean square error (ubMSE) relative to watershed-scale in situ observations, with the ubMSE separated into contributions from the subseasonal (SMshort), mean seasonal (SMseas), and inter-annual (SMlong) soil moisture dynamics. For near-surface soil moisture, the average ubMSE for Catchment without assimilation was (1.8 × 10−3 m3 m−3)2, of which 19 % was in SMlong, 26 % in SMseas, and 55 % in SMshort. The AMSR-E assimilation significantly reduced the total ubMSE at every site, with an average reduction of 33 %. Of this ubMSE reduction, 37 % occurred in SMlong, 24 % in SMseas, and 38 % in SMshort. For root-zone soil moisture, in situ observations were available at one site only, and the near-surface and root-zone results were very similar at this site. These results suggest that, in addition to the well-reported improvements in SMshort, assimilating a sufficiently long soil moisture data record can also improve the model representation of important long-term events, such as droughts. The improved agreement between the modeled and in situ SMseas is harder to interpret, given that mean seasonal cycle errors are systematic, and systematic errors are not typically targeted by (bias-blind) data assimilation. Finally, the use of 1-year subsets of the AMSR-E and Catchment soil moisture for estimating the observation-bias correction (rescaling) parameters is investigated. It is concluded that when only 1 year of data are available, the associated uncertainty in the rescaling parameters should not greatly reduce the average benefit gained from data assimilation, although locally and in extreme years there is a risk of increased errors.

Impact of Incorrect Model Error Assumptions on the Sequential Assimilation of Remotely Sensed Surface Soil Moisture

2006 ◽  
Vol 7 (3) ◽  
pp. 421-432 ◽  
Author(s):  
Wade T. Crow ◽  
Emiel Van Loon
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Land Surface ◽  
Surface Soil ◽  
Random Noise ◽  
Land Surface Model ◽  
Model Error ◽  
Surface Model ◽  
Surface Soil Moisture ◽  
Incorrect Model

Abstract Data assimilation approaches require some type of state forecast error covariance information in order to optimally merge model predictions with observations. The ensemble Kalman filter (EnKF) dynamically derives such information through a Monte Carlo approach and the introduction of random noise in model states, fluxes, and/or forcing data. However, in land data assimilation, relatively little guidance exists concerning strategies for selecting the appropriate magnitude and/or type of introduced model noise. In addition, little is known about the sensitivity of filter prediction accuracy to (potentially) inappropriate assumptions concerning the source and magnitude of modeling error. Using a series of synthetic identical twin experiments, this analysis explores the consequences of making incorrect assumptions concerning the source and magnitude of model error on the efficiency of assimilating surface soil moisture observations to constrain deeper root-zone soil moisture predictions made by a land surface model. Results suggest that inappropriate model error assumptions can lead to circumstances in which the assimilation of surface soil moisture observations actually degrades the performance of a land surface model (relative to open-loop assimilations that lack a data assimilation component). Prospects for diagnosing such circumstances and adaptively correcting the culpable model error assumptions using filter innovations are discussed. The dual assimilation of both runoff (from streamflow) and surface soil moisture observations appears to offer a more robust assimilation framework where incorrect model error assumptions are more readily diagnosed via filter innovations.

scholarly journals Development of a Unified Land Model for Prediction of Surface Hydrology and Land–Atmosphere Interactions

2011 ◽  
Vol 12 (6) ◽  
pp. 1299-1320 ◽  
Author(s):  
Ben Livneh ◽  
Pedro J. Restrepo ◽  
Dennis P. Lettenmaier
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Climate Models ◽  
A Priori ◽  
Model Performance ◽  
Surface Flux ◽  
Surface Fluxes ◽  
Frozen Soil ◽  
Surface Model ◽  
Soil Moisture Accounting

Abstract A unified land model (ULM) is described that combines the surface flux parameterizations in the Noah land surface model (used in most of NOAA’s coupled weather and climate models) with the Sacramento Soil Moisture Accounting model (Sac; used for hydrologic prediction within the National Weather Service). The motivation was to develop a model that has a history of strong hydrologic performance while having the ability to be run in the coupled land–atmosphere environment. ULM takes the vegetation, snow model, frozen soil, and evapotranspiration schemes from Noah and merges them with the soil moisture accounting scheme from Sac. ULM surface fluxes, soil moisture, and streamflow simulations were evaluated through comparisons with observations from the Ameriflux (surface flux), Illinois Climate Network (soil moisture), and Model Parameter Estimation Experiment (MOPEX; streamflow) datasets. Initially, a priori parameters from Sac and Noah were used, which resulted in ULM surface flux simulations that were comparable to those produced by Noah (Sac does not predict surface energy fluxes). ULM with the a priori parameters had streamflow simulation skill that was generally similar to Sac’s, although it was slightly better (worse) for wetter (more arid) basins. ULM model performance using a set of parameters identified via a Monte Carlo search procedure lead to substantial improvements relative to the a priori parameters. A scheme for transfer of parameters from streamflow simulations to nearby flux and soil moisture measurement points was also evaluated; this approach did not yield conclusive improvements relative to the a priori parameters.

scholarly journals The effect of satellite-derived surface soil moisture and leaf area index land data assimilation on streamflow simulations over France

2017 ◽  
Vol 21 (4) ◽  
pp. 2015-2033 ◽  
Author(s):  
David Fairbairn ◽  
Alina Lavinia Barbu ◽  
Adrien Napoly ◽  
Clément Albergel ◽  
Jean-François Mahfouf ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Leaf Area Index ◽  
Land Surface ◽  
River Discharge ◽  
Surface Soil ◽  
Land Surface Model ◽  
Surface Model ◽  
Surface Soil Moisture ◽  
Area Index

Abstract. This study evaluates the impact of assimilating surface soil moisture (SSM) and leaf area index (LAI) observations into a land surface model using the SAFRAN–ISBA–MODCOU (SIM) hydrological suite. SIM consists of three stages: (1) an atmospheric reanalysis (SAFRAN) over France, which forces (2) the three-layer ISBA land surface model, which then provides drainage and runoff inputs to (3) the MODCOU hydro-geological model. The drainage and runoff outputs from ISBA are validated by comparing the simulated river discharge from MODCOU with over 500 river-gauge observations over France and with a subset of stations with low-anthropogenic influence, over several years. This study makes use of the A-gs version of ISBA that allows for physiological processes. The atmospheric forcing for the ISBA-A-gs model underestimates direct shortwave and long-wave radiation by approximately 5 % averaged over France. The ISBA-A-gs model also substantially underestimates the grassland LAI compared with satellite retrievals during winter dormancy. These differences result in an underestimation (overestimation) of evapotranspiration (drainage and runoff). The excess runoff flowing into the rivers and aquifers contributes to an overestimation of the SIM river discharge. Two experiments attempted to resolve these problems: (i) a correction of the minimum LAI model parameter for grasslands and (ii) a bias-correction of the model radiative forcing. Two data assimilation experiments were also performed, which are designed to correct random errors in the initial conditions: (iii) the assimilation of LAI observations and (iv) the assimilation of SSM and LAI observations. The data assimilation for (iii) and (iv) was done with a simplified extended Kalman filter (SEKF), which uses finite differences in the observation operator Jacobians to relate the observations to the model variables. Experiments (i) and (ii) improved the median SIM Nash scores by about 9 % and 18 % respectively. Experiment (iii) reduced the LAI phase errors in ISBA-A-gs but had little impact on the discharge Nash efficiency of SIM. In contrast, experiment (iv) resulted in spurious increases in drainage and runoff, which degraded the median discharge Nash efficiency by about 7 %. The poor performance of the SEKF originates from the observation operator Jacobians. These Jacobians are dampened when the soil is saturated and when the vegetation is dormant, which leads to positive biases in drainage and/or runoff and to insufficient corrections during winter, respectively. Possible ways to improve the model are discussed, including a new multi-layer diffusion model and a more realistic response of photosynthesis to temperature in mountainous regions. The data assimilation should be advanced by accounting for model and forcing uncertainties.

Cosmic Ray Neutron Sensing: Integration with land surface modelling using data assimilation for improved field-scale soil moisture estimates

2020 ◽  
Author(s):  
Amol Patil ◽  
Benjamin Fersch ◽  
Harrie-Jan Hendricks-Franssen ◽  
Harald Kunstmann
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Spatial Resolution ◽  
Land Surface ◽  
Root Zone ◽  
Cosmic Ray ◽  
Soil Parameters ◽  
Surface Model ◽  
Field Scale ◽  
Spatio Temporal

<p>Soil moisture is a key variable in atmospheric modelling to resolve the partitioning of net radiation into sensible and latent heat fluxes. Therefore, high resolution spatio-temporal soil moisture estimation is getting growing attention in this decade. The recent developments to observe soil moisture at field scale (170 to 250 m spatial resolution) using Cosmic Ray Neutron Sensing (CRNS) technique has created new opportunities to better resolve land surface atmospheric interactions; however, many challenges remain such as spatial resolution mismatch and estimation uncertainties. Our study couples the Noah-MP land surface model to the Data Assimilation Research Testbed (DART) for assimilating CRN intensities to update model soil moisture. For evaluation, the spatially distributed Noah-MP was set up to simulate the land surface variables at 1 km horizontal resolution for the Rott and Ammer catchments in southern Germany. The study site comprises the TERENO-preAlpine observatory with five CRNS stations and additional CRNS measurements for summer 2019 operated by our Cosmic Sense research group. We adjusted the soil parametrization in Noah-MP to allow the usage of EU soil data along with Mualem-van Genuchten soil hydraulic parameters. We use independent observations from extensive soil moisture sensor network (SoilNet) within the vicinity of CRNS sensors for validation. Our detailed synthetic and real data experiments are evaluated for the analysis of the spatio-temporal changes in updated root zone soil moisture and for implications on the energy balance component of Noah-MP. Furthermore, we present possibilities to estimate root zone soil parameters within the data assimilation framework to enhance standalone model performance.</p>

Multi-scale assimilation of SMAP data: comparison between land surface and land surface-subsurface model

2020 ◽  
Author(s):  
Haojin Zhao ◽  
Roland Baatz ◽  
Carsten Montzka ◽  
Harry Vereecken ◽  
Harrie-Jan Hendricks Franssen
Keyword(s):  
Parameter Estimation ◽  
Soil Moisture ◽  
Data Assimilation ◽  
Simulation Model ◽  
Land Surface ◽  
Cosmic Ray ◽  
Remotely Sensed ◽  
Surface Model ◽  
Systematic Bias

<p>Soil moisture plays an important role in the coupled water and energy cycles of the terrestrial system. However, the characterization of soil moisture at the large spatial scale is far from trivial. To cope with this challenge, the combination of data from different sources (in situ measurements by cosmic ray neutron sensors, remotely sensed soil moisture and simulated soil moisture by models) is pursued. This is done by multiscale data assimilation, to take the different resolutions of the data into account. A large number of studies on the assimilation of remotely sensed soil moisture in land surface models has been published, which show in general only a limited improvement in the characterization of root zone soil moisture, and no improvement in the characterization of evapotranspiration. In this study it was investigated whether an improved modelling of soil moisture content, using a simulation model where the interactions between the land surface, surface water and groundwater are better represented, can contribute to extracting more information from SMAP data. In this study over North-Rhine-Westphalia, the assimilation of remotely sensed soil moisture from SMAP in the coupled land surface-subsurface model TSMP was tested. Results were compared with the assimilation in the stand-alone land surface model CLM. It was also tested whether soil hydraulic parameter estimation in combination with state updating could give additional skill compared to assimilation in CLM stand-alone and without parameter updating. Results showed that modelled soil moisture by TSMP did not show a systematic bias compared to SMAP, whereas CLM was systematically wetter than TSMP. Therefore, no prior bias correction was needed in the data assimilation. The results illustrate how the difference in simulation model and parameter estimation result in significantly different estimated soil moisture contents and evapotranspiration.  </p>

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).

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