scholarly journals Initializing numerical weather prediction models with satellite-derived surface soil moisture: Data assimilation experiments with ECMWF's Integrated Forecast System and the TMI soil moisture data set

2007 ◽  
Vol 112 (D3) ◽  
Author(s):  
M. Drusch
Keyword(s):  
Soil Moisture ◽  
Numerical Weather Prediction ◽  
Prediction Models ◽  
Surface Soil ◽  
Weather Prediction ◽  
Surface Soil Moisture ◽  
Forecast System ◽  
Data Set ◽  
Soil Moisture Data ◽  
Numerical Weather Prediction Models

scholarly journals Assimilation of SCATSAR Soil Wetness Index in SURFEX 8.0 to improve weather forecasts

2019 ◽  
Author(s):  
Stefan Schneider ◽  
Bernhard Bauer-Marschallinger
Keyword(s):  
Soil Moisture ◽  
Prediction Models ◽  
Surface Soil ◽  
Weather Prediction ◽  
Soil Layer ◽  
New Combination ◽  
Data Set ◽  
Weather Forecasts ◽  
Soil Moisture Data ◽  
Soil Model

Abstract. To date, in numerical weather prediction models it has only been possible to assimilate surface soil moisture data. Due to the structure of this surface soil layer in soil models, the effect of the assimilation vanishes fast. This results in a small impact on model performance. Here we present a combination of two new developments to overcome this problem. On the one hand, a new satellite based soil moisture data set is assimilated that combines the advantages of two different sensors (MetOp ASCAT and Sentinel-1 SAR) and the so-called T-value approach to estimate the soil moisture content of deeper soil layers. On the other hand, an advanced version of the well-established SURFEX soil model data assimilation software is used to ingest this new data source for improved short-range weather forecasts. Comparisons of the two data sets from satellite and soil model indicate that the estimation of deep soil moisture from superficial measurements produces reasonable estimates down to 0.5 m. Assimilation experiments with a simplified Extended Kalman Filter for a model domain covering Austria shows the benefit of this new combination with improved verification scores for temperature and relative humidity forecasts at 2 m above ground.

scholarly journals Error characterisation of global active and passive microwave soil moisture data sets

2010 ◽  
Vol 7 (4) ◽  
pp. 5621-5645 ◽  
Author(s):  
W. A. Dorigo ◽  
K. Scipal ◽  
R. M. Parinussa ◽  
Y. Y. Liu ◽  
W. Wagner ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Prediction Models ◽  
Weather Prediction ◽  
Passive Microwave ◽  
Data Sets ◽  
Independent Data ◽  
Vegetation Density ◽  
Data Set ◽  
Soil Moisture Data

Abstract. Understanding the error structures of remotely sensed soil moisture products is essential for correctly interpreting observed variations and trends in the data or assimilating them in hydrological or numerical weather prediction models. Nevertheless, a spatially coherent assessment of the quality of the various globally available data sets is often hampered by the limited availability over space and time of reliable in-situ measurements. This study explores the triple collocation error estimation technique for assessing the relative quality of several globally available soil moisture products from active (ASCAT) and passive (AMSR-E and SSM/I) microwave sensors. The triple collocation technique is a powerful tool to estimate the root mean square error while simultaneously solving for systematic differences in the climatologies of a set of three independent data sources. In addition to the scatterometer and radiometer data sets, we used the ERA-Interim and GLDAS-NOAH reanalysis soil moisture data sets as a third, independent reference. The prime objective is to reveal trends in uncertainty related to different observation principles (passive versus active), the use of different frequencies (C-, X-, and Ku-band) for passive microwave observations, and the choice of the independent reference data set (ERA-Interim versus GLDAS-NOAH). The results suggest that the triple collocation method provides realistic error estimates. Observed spatial trends agree well with the existing theory and studies on the performance of different observation principles and frequencies with respect to land cover and vegetation density. In addition, if all theoretical prerequisites are fulfilled (e.g. a sufficiently large number of common observations is available and errors of the different data sets are uncorrelated) the errors estimated for the remote sensing products are hardly influenced by the choice of the third independent data set. The results obtained in this study can help us in developing adequate strategies for the combined use of various scatterometer and radiometer-based soil moisture data sets, e.g. for improved flood forecast modelling or the generation of superior multi-mission long-term soil moisture data sets.

scholarly journals Impact of Soil Moisture Data Characteristics on the Sensitivity to Crop Yields Under Drought and Excess Moisture Conditions

2019 ◽  
Vol 11 (4) ◽  
pp. 372 ◽  
Author(s):  
Catherine Champagne ◽  
Jenelle White ◽  
Aaron Berg ◽  
Stephane Belair ◽  
Marco Carrera
Keyword(s):  
Soil Moisture ◽  
Surface Soil ◽  
Crop Yields ◽  
Agricultural Drought ◽  
Data Sets ◽  
Surface Soil Moisture ◽  
Short Baseline ◽  
Data Set ◽  
Soil Moisture Data ◽  
The Relationship

Soil moisture is often considered a direct way of quantifying agricultural drought since it is a measure of the availability of water to support crop growth. Measurements of soil moisture at regional scales have traditionally been sparse, but advances in land surface modelling and the development of satellite technology to indirectly measure surface soil moisture has led to the emergence of a number of national and global soil moisture data sets that can provide insight into the dynamics of agricultural drought. Droughts are often defined by normal conditions for a given time and place; as a result, data sets used to quantify drought need a representative baseline of conditions in order to accurately establish a normal. This presents a challenge when working with earth observation data sets which often have very short baselines for a single instrument. This study assessed three soil moisture data sets: a surface satellite soil moisture data set from the Soil Moisture and Ocean Salinity (SMOS) mission operating since 2010; a blended surface satellite soil moisture data set from the European Space Agency Climate Change Initiative (ESA-CCI) that has a long history and a surface and root zone soil moisture data set from the Canadian Meteorology Centre (CMC)’s Regional Deterministic Prediction System (RDPS). An iterative chi-squared statistical routine was used to evaluate each data set’s sensitivity to canola yields in Saskatchewan, Canada. The surface soil moisture from all three data sets showed a similar temporal trend related to crop yields, showing a negative impact on canola yields when soil moisture exceeded a threshold in May and June. The strength and timing of this relationship varied with the accuracy and statistical properties of the data set, with the SMOS data set showing the strongest relationship (peak X2 = 170 for Day of Year 145), followed by the ESA-CCI (peak X2 = 89 on Day of Year 129) and then the RDPS (peak X2 = 65 on Day of Year 129). Using short baseline soil moisture data sets can produce consistent results compared to using a longer data set, but the characteristics of the years used for the baseline are important. Soil moisture baselines of 18–20 years or more are needed to reliably estimate the relationship between high soil moisture and high yielding years. For the relationship between low soil moisture and low yielding years, a shorter baseline can be used, with reliable results obtained when 10–15 years of data are available, but with reasonably consistent results obtained with as few as 7 years of data. This suggests that the negative impacts of drought on agriculture may be reliably estimated with a relatively short baseline of data.

scholarly journals Parametrizations of Liquid and Ice Clouds’ Optical Properties in Operational Numerical Weather Prediction Models

Atmosphere ◽  
2021 ◽  
Vol 12 (1) ◽  
pp. 89
Author(s):  
Harel. B. Muskatel ◽  
Ulrich Blahak ◽  
Pavel Khain ◽  
Yoav Levi ◽  
Qiang Fu
Keyword(s):  
Optical Properties ◽  
Numerical Weather Prediction ◽  
Radiation Transfer ◽  
Prediction Models ◽  
Weather Prediction ◽  
Effective Size ◽  
Asymmetry Factor ◽  
Numerical Weather ◽  
Numerical Weather Prediction Models ◽  
Spectral Averaging

Parametrization of radiation transfer through clouds is an important factor in the ability of Numerical Weather Prediction models to correctly describe the weather evolution. Here we present a practical parameterization of both liquid droplets and ice optical properties in the longwave and shortwave radiation. An advanced spectral averaging method is used to calculate the extinction coefficient, single scattering albedo, forward scattered fraction and asymmetry factor (bext, v, f, g), taking into account the nonlinear effects of light attenuation in the spectral averaging. An ensemble of particle size distributions was used for the ice optical properties calculations, which enables the effective size range to be extended up to 570 μm and thus be applicable for larger hydrometeor categories such as snow, graupel, and rain. The new parameterization was applied both in the COSMO limited-area model and in ICON global model and was evaluated by using the COSMO model to simulate stratiform ice and water clouds. Numerical weather prediction models usually determine the asymmetry factor as a function of effective size. For the first time in an operational numerical weather prediction (NWP) model, the asymmetry factor is parametrized as a function of aspect ratio. The method is generalized and is available on-line to be readily applied to any optical properties dataset and spectral intervals of a wide range of radiation transfer models and applications.

Forecasting wave height probabilities with numerical weather prediction models

2005 ◽  
Vol 32 (14-15) ◽  
pp. 1841-1863 ◽  
Author(s):  
Mark S. Roulston ◽  
Jerome Ellepola ◽  
Jost von Hardenberg ◽  
Leonard A. Smith
Keyword(s):  
Wave Height ◽  
Numerical Weather Prediction ◽  
Prediction Models ◽  
Weather Prediction ◽  
Numerical Weather ◽  
Numerical Weather Prediction Models

scholarly journals From near-surface to root-zone soil moisture using an exponential filter: an assessment of the method based on in-situ observations and model simulations

2008 ◽  
Vol 12 (6) ◽  
pp. 1323-1337 ◽  
Author(s):  
C. Albergel ◽  
C. Rüdiger ◽  
T. Pellarin ◽  
J.-C. Calvet ◽  
N. Fritz ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Soil Water ◽  
Surface Soil ◽  
Root Zone ◽  
Synthetic Data ◽  
Surface Soil Moisture ◽  
Data Set ◽  
Water Index ◽  
In Situ Observations

Abstract. A long term data acquisition effort of profile soil moisture is under way in southwestern France at 13 automated weather stations. This ground network was developed in order to validate remote sensing and model soil moisture estimates. In this paper, both those in situ observations and a synthetic data set covering continental France are used to test a simple method to retrieve root zone soil moisture from a time series of surface soil moisture information. A recursive exponential filter equation using a time constant, T, is used to compute a soil water index. The Nash and Sutcliff coefficient is used as a criterion to optimise the T parameter for each ground station and for each model pixel of the synthetic data set. In general, the soil water indices derived from the surface soil moisture observations and simulations agree well with the reference root-zone soil moisture. Overall, the results show the potential of the exponential filter equation and of its recursive formulation to derive a soil water index from surface soil moisture estimates. This paper further investigates the correlation of the time scale parameter T with soil properties and climate conditions. While no significant relationship could be determined between T and the main soil properties (clay and sand fractions, bulk density and organic matter content), the modelled spatial variability and the observed inter-annual variability of T suggest that a weak climate effect may exist.

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