Verification of a Mesoscale Data-Assimilation and Forecasting System for the Oklahoma City Area during the Joint Urban 2003 Field Project

Abstract The National Center for Atmospheric Research (NCAR) and the U.S. Army Test and Evaluation Command have developed a multiscale, rapid-cycling, real-time, four-dimensional data-assimilation and forecasting system that has been in operational use at five Army test ranges since 2001. This system was employed to provide operational modeling support for the Joint Urban 2003 (JU2003) Dispersion Experiment, conducted in Oklahoma City, Oklahoma, during July 2003. To better support this mission, modifications were made to the nonlocal boundary layer (BL) parameterization (known as the Medium Range Forecast scheme) of the fifth-generation Pennsylvania State University–NCAR Mesoscale Model, in order to improve BL forecasts. The NCEP–Oregon State University–Air Force–Hydrologic Research Laboratory land surface model was also improved to better represent urban forcing. Verification of the operational model runs and retrospectively simulated cases show 1) a significantly reduced low bias in the forecast surface wind speed and 2) more realistic daytime BL heights. During JU2003, the forecast urban heat island, urban dry bubble, and urban BL height agree reasonably well with observations and conceptual models. An analysis of three-dimensional atmospheric structures, based on model analyses for eight clear-sky days during the field program, reveals some interesting features of the Oklahoma City urban BL, including complex thermally induced circulations and associated convergence/divergence zones, a nocturnal thermal shadow downwind of the urban area, and the reduction of low-level jet wind speeds by more vigorous nocturnal mixing over the city.

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Intercomparison of Mesoscale Model Simulations of the Daytime Valley Wind System

Monthly Weather Review ◽

10.1175/2010mwr3523.1 ◽

2011 ◽

Vol 139 (5) ◽

pp. 1389-1409 ◽

Cited By ~ 42

Author(s):

Juerg Schmidli ◽

Brian Billings ◽

Fotini K. Chow ◽

Stephan F. J. de Wekker ◽

James Doyle ◽

...

Keyword(s):

Land Surface ◽

Mesoscale Model ◽

Numerical Models ◽

Surface Wind ◽

Sensible Heat Flux ◽

Surface Wind Speed ◽

Time Shift ◽

Wind System ◽

Valley Wind ◽

The Mean

Three-dimensional simulations of the daytime thermally induced valley wind system for an idealized valley–plain configuration, obtained from nine nonhydrostatic mesoscale models, are compared with special emphasis on the evolution of the along-valley wind. The models use the same initial and lateral boundary conditions, and standard parameterizations for turbulence, radiation, and land surface processes. The evolution of the mean along-valley wind (averaged over the valley cross section) is similar for all models, except for a time shift between individual models of up to 2 h and slight differences in the speed of the evolution. The analysis suggests that these differences are primarily due to differences in the simulated surface energy balance such as the dependence of the sensible heat flux on surface wind speed. Additional sensitivity experiments indicate that the evolution of the mean along-valley flow is largely independent of the choice of the dynamical core and of the turbulence parameterization scheme. The latter does, however, have a significant influence on the vertical structure of the boundary layer and of the along-valley wind. Thus, this ideal case may be useful for testing and evaluation of mesoscale numerical models with respect to land surface–atmosphere interactions and turbulence parameterizations.

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Homogenization and Assessment of Observed Near-Surface Wind Speed Trends over Spain and Portugal, 1961–2011*

Journal of Climate ◽

10.1175/jcli-d-13-00652.1 ◽

2014 ◽

Vol 27 (10) ◽

pp. 3692-3712 ◽

Cited By ~ 81

Author(s):

Cesar Azorin-Molina ◽

Sergio M. Vicente-Serrano ◽

Tim R. McVicar ◽

Sonia Jerez ◽

Arturo Sanchez-Lorenzo ◽

...

Keyword(s):

Wind Speed ◽

Mesoscale Model ◽

Surface Wind ◽

Surface Wind Speed ◽

State University ◽

Evaporative Demand ◽

Near Surface ◽

Fifth Generation ◽

Simulated Series ◽

Increasing Trends

Abstract Near-surface wind speed trends recorded at 67 land-based stations across Spain and Portugal for 1961–2011, also focusing on the 1979–2008 subperiod, were analyzed. Wind speed series were subjected to quality control, reconstruction, and homogenization using a novel procedure that incorporated the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5)-simulated series as reference. The resultant series show a slight downward trend for both 1961–2011 (−0.016 m s−1 decade−1) and 1979–2008 (−0.010 m s−1 decade−1). However, differences between seasons with declining values in winter and spring, and increasing trends in summer and autumn, were observed. Even though wind stilling affected 77.8% of the stations in winter and 66.7% in spring, only roughly 40% of the declining trends were statistically significant at the p < 0.10 level. On the contrary, increasing trends appeared in 51.9% of the stations in summer and 57.4% in autumn, with also around 40% of the positive trends statistically significant at the p < 0.10 level. In this article, the authors also investigated (i) the possible impact of three atmospheric indices on the observed trends and (ii) the role played by the urbanization growth in the observed decline. An accurate homogenization and assessment of the long-term trends of wind speed is crucial for many fields such as wind energy (e.g., power generation) and agriculture–hydrology (e.g., evaporative demand).

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Constraining a land surface model with multiple observations by application of the MPI-Carbon Cycle Data Assimilation System

10.5194/gmd-2015-263 ◽

2016 ◽

Cited By ~ 3

Author(s):

G. J. Schürmann ◽

T. Kaminski ◽

C. Köstler ◽

N. Carvalhais ◽

M. Voßbeck ◽

...

Keyword(s):

Data Assimilation ◽

Carbon Cycle ◽

Land Surface ◽

Data Stream ◽

Atmospheric Co2 ◽

Surface Model ◽

Terrestrial Biosphere ◽

Multiple Observations ◽

Data Assimilation System ◽

Assimilation System

Abstract. We describe the Max Planck Institute Carbon Cycle Data Assimilation System (MPI-CCDAS) built around the tangent-linear version of the land surface scheme of the MPI-Earth System Model v1 (JSBACH). The simulated terrestrial biosphere processes (phenology and carbon balance) were constrained by observations of the fraction of photosynthetically active radiation (TIP-FAPAR product) and by observations of atmospheric CO2 at a global set of monitoring stations for the years 2005–2009. The system successfully, and computationally efficiently, improved average foliar area and northern extra-tropical seasonality of foliar area when constrained by TIP-FAPAR. Global net and gross carbon fluxes were improved when constrained by atmospheric CO2, although the system tended to underestimate tropical productivity. Assimilating both data streams jointly allowed the MPI-CCDAS to match both observations (TIP-FAPAR and atmospheric CO2) equally well as the single data stream assimilation cases, therefore overall increasing the appropriateness of the resultant parameter values and biosphere dynamics. Our study thus highlights the role of the TIP-FAPAR product in stabilising the underdetermined atmospheric inversion problem and demonstrates the value of multiple-data stream assimilation for the simulation of terrestrial biosphere dynamics. The constraint on regional gross and net CO2 flux patterns is limited through the parametrisation of the biosphere model. We expect improvement on that aspect through a refined initialisation strategy and inclusion of further biosphere observations as constraints.

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Impact of Four-Dimensional Variational Data Assimilation of Atmospheric Motion Vectors on Tropical Cyclone Track Forecasts

Weather and Forecasting ◽

10.1175/waf940.1 ◽

2006 ◽

Vol 21 (4) ◽

pp. 663-669 ◽

Cited By ~ 11

Author(s):

Dongliang Wang ◽

Xudong Liang ◽

Yihong Duan ◽

Johnny C. L. Chan

Keyword(s):

Tropical Cyclone ◽

Data Assimilation ◽

Mesoscale Model ◽

State University ◽

Motion Vectors ◽

Geostationary Meteorological Satellite ◽

Atmospheric Motion ◽

Atmospheric Motion Vectors ◽

Data Assimilation Technique ◽

The Impact

Abstract The fifth-generation Pennsylvania State University–National Center for Atmospheric Research nonhydrostatic Mesoscale Model is employed to evaluate the impact of the Geostationary Meteorological Satellite-5 water vapor and infrared atmospheric motion vectors (AMVs), incorporated with the four-dimensional variational (4DVAR) data assimilation technique, on tropical cyclone (TC) track predictions. Twenty-two cases from eight different TCs over the western North Pacific in 2002 have been examined. The 4DVAR assimilation of these satellite-derived wind observations leads to appreciable improvements in the track forecasts, with average reductions in track error of ∼5% at 12 h, 12% at 24 h, 10% at 36 h, and 7% at 48 h. Preliminary results suggest that the improvement depends on the quantity of the AMV data available for assimilation.

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Impact of Parameterization of Physical Processes on Simulation of Track and Intensity of Tropical Cyclone Nargis (2008) with WRF-NMM Model

The Scientific World JOURNAL ◽

10.1100/2012/671437 ◽

2012 ◽

Vol 2012 ◽

pp. 1-18 ◽

Cited By ~ 19

Author(s):

Sujata Pattanayak ◽

U. C. Mohanty ◽

Krishna K. Osuri

Keyword(s):

Land Surface ◽

Mesoscale Model ◽

Initial Conditions ◽

Surface Wind ◽

India Meteorological Department ◽

Heat Fluxes ◽

Cumulus Convection ◽

Cyclonic Storm ◽

Parameterization Schemes ◽

Cyclone Nargis

The present study is carried out to investigate the performance of different cumulus convection, planetary boundary layer, land surface processes, and microphysics parameterization schemes in the simulation of a very severe cyclonic storm (VSCS) Nargis (2008), developed in the central Bay of Bengal on 27 April 2008. For this purpose, the nonhydrostatic mesoscale model (NMM) dynamic core of weather research and forecasting (WRF) system is used. Model-simulated track positions and intensity in terms of minimum central mean sea level pressure (MSLP), maximum surface wind (10 m), and precipitation are verified with observations as provided by the India Meteorological Department (IMD) and Tropical Rainfall Measurement Mission (TRMM). The estimated optimum combination is reinvestigated with six different initial conditions of the same case to have better conclusion on the performance of WRF-NMM. A few more diagnostic fields like vertical velocity, vorticity, and heat fluxes are also evaluated. The results indicate that cumulus convection play an important role in the movement of the cyclone, and PBL has a crucial role in the intensification of the storm. The combination of Simplified Arakawa Schubert (SAS) convection, Yonsei University (YSU) PBL, NMM land surface, and Ferrier microphysics parameterization schemes in WRF-NMM give better track and intensity forecast with minimum vector displacement error.

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

10.5194/hess-2020-359 ◽

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.

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

10.20944/preprints201904.0216.v1 ◽

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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Toward a South America Land Data Assimilation System: Aspects of land surface model spin-up using the Simplified Simple Biosphere

Journal of Geophysical Research Atmospheres ◽

10.1029/2005jd006297 ◽

2006 ◽

Vol 111 (D17) ◽

Cited By ~ 17

Author(s):

L. Gustavo Goncalves de Goncalves ◽

William James Shuttleworth ◽

Eleanor J. Burke ◽

Paul Houser ◽

David L. Toll ◽

...

Keyword(s):

Data Assimilation ◽

South America ◽

Land Surface ◽

Land Surface Model ◽

Surface Model ◽

Land Data Assimilation ◽

Land Data Assimilation System ◽

Data Assimilation System ◽

Assimilation System

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Data assimilation of GRACE terrestrial water storage to improve sea level rise estimates and isolate anthropogenic influences

10.5194/egusphere-egu21-6380 ◽

2021 ◽

Author(s):

Ann Scheliga ◽

Manuela Girotto

Keyword(s):

Data Assimilation ◽

Sea Level Rise ◽

Sea Level ◽

Land Surface ◽

Water Budget ◽

Water Storage ◽

Surface Model ◽

Terrestrial Water Storage ◽

Terrestrial Water ◽

Land Hydrology

Sea level rise (SLR) projections rely on the accurate and precise closure of Earth&#8217;s water budget. The Gravity Recovery and Climate Experiment (GRACE) mission has provided global-coverage observations of terrestrial water storage (TWS) anomalies that improve accounting of ice and land hydrology changes and how these changes contribute to sea level rise. The contribution of land hydrology TWS changes to sea level rise is much smaller and less certain than contributions from glacial melt and thermal expansion. Although land hydrology TWS plays a smaller role, it is still important to investigate to improve the precision of the overall global water budget. This study analyzes how data assimilation techniques improve estimates of the land hydrology contribution to sea level rise. To achieve this, three global TWS datasets were analyzed: (1) GRACE TWS observations alone, (2) TWS estimates from the model-only simulation using Catchment Land Surface Model, and (3) TWS estimates from a data assimilation product of (1) and (2). We compared the data assimilation product with the GRACE observations alone and the model-only simulation to isolate the contribution to sea level rise from anthropogenic activities. We assumed a balanced water budget between land hydrology and the ocean, thus changes in global TWS are considered equal and opposite to sea level rise contribution. &#160;Over the period of 2003-2016, we found sea level rise contributions from each dataset of +0.35 mm SLR eq/yr for GRACE, -0.34 mm SLR eq/yr for model-only, and a +0.09 mm SLR eq/yr for DA (reported as the mean linear trend). Our results indicate that the model-only simulation is not capturing important hydrologic processes. These are likely anthropogenic driven, indicating direct anthropogenic and climate-driven TWS changes play a substantial role in TWS contribution to SLR.

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The Role of Different Sources of Uncertainty in Data Assimilation with Coupled Land Surface - Subsurface Models

10.5194/egusphere-egu21-14491 ◽

2021 ◽

Author(s):

Eduardo Emilio Sanchez-Leon ◽

Natascha Brandhorst ◽

Bastian Waldowski ◽

Ching Pui Hung ◽

Insa Neuweiler ◽

...

Keyword(s):

Plant Physiology ◽

Data Assimilation ◽

Land Surface ◽

Reference Model ◽

Land Surface Model ◽

Open Loop ◽

Surface Model ◽

Model Parameters ◽

Area Index ◽

Sources Of Uncertainty

The success of data assimilation systems strongly depends on the suitability of the generated ensembles. While in theory data assimilation should correct the states of an ensemble of models, especially if model parameters are included in the update, its effectiveness will depend on many factors, such as ensemble size, ensemble spread, and the proximity of the prior ensemble simulations to the data. In a previous study, we generated an ensemble-based data-assimilation framework to update model states and parameters of a coupled land surface-subsurface model. As simulation system we used the Terrestrial Systems Modeling Platform TerrSysMP, with the community land-surface model (CLM) coupled to the subsurface model Parflow. In this work, we used the previously generated ensemble to assess the effect of uncertain input forcings (i.e. precipitation), unknown subsurface parameterization, and/or plant physiology in data assimilation. The model domain covers a rectangular area of 1&#215;5km2, with a uniform depth of 50m. The subsurface material is divided into four units, and the top soil layers consist of three different soil types with different vegetation. Streams are defined along three of the four boundaries of the domain. For data assimilation, we used the TerrsysMP PDAF framework. We defined a series of data assimilation experiments in which sources of uncertainty were considered individually, and all additional settings of the ensemble members matched those of the reference. To evaluate the effect of all sources of uncertainty combined, we designed an additional test in which the input forcings, subsurface parameters, and the leaf area index of the ensemble were all perturbed. In all these tests, the reference model had homogenous subsurface units and the same grid resolution as all models of the ensemble. We used point measurements of soil moisture in all data assimilation experiments. We concluded that precipitation dominates the dynamics of the simulations, and perturbing the precipitation fields for the ensemble have a major impact in the performance of the assimilation. Still, considerable improvements are observed compared to open-loop simulations. In contrast, the effect of variable plant physiology was minimal, with no visible improvement in relevant fluxes such as evapotranspiration. As expected, improved ensemble predictions are propagated longer in time when parameters are included in the update.

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