scholarly journals Consistent retrieval of land surface radiation products from EO, including traceable uncertainty estimates

2017 ◽  
Vol 14 (9) ◽  
pp. 2527-2541 ◽  
Author(s):  
Thomas Kaminski ◽  
Bernard Pinty ◽  
Michael Voßbeck ◽  
Maciej Lopatka ◽  
Nadine Gobron ◽  
...  
Keyword(s):  
Land Surface ◽  
Prediction Models ◽  
Weather Prediction ◽  
Processing System ◽  
Surface Radiation ◽  
Research Centre ◽  
Soil System ◽  
Global Models ◽  
Area Index ◽  
Uncertainty Estimates

Abstract. Earth observation (EO) land surface products have been demonstrated to provide a constraint on the terrestrial carbon cycle that is complementary to the record of atmospheric carbon dioxide. We present the Joint Research Centre Two-stream Inversion Package (JRC-TIP) for retrieval of variables characterising the state of the vegetation–soil system. The system provides a set of land surface variables that satisfy all requirements for assimilation into the land component of climate and numerical weather prediction models. Being based on a 1-D representation of the radiative transfer within the canopy–soil system, such as those used in the land surface components of advanced global models, the JRC-TIP products are not only physically consistent internally, but they also achieve a high degree of consistency with these global models. Furthermore, the products are provided with full uncertainty information. We describe how these uncertainties are derived in a fully traceable manner without any hidden assumptions from the input observations, which are typically broadband white sky albedo products. Our discussion of the product uncertainty ranges, including the uncertainty reduction, highlights the central role of the leaf area index, which describes the density of the canopy. We explain the generation of products aggregated to coarser spatial resolution than that of the native albedo input and describe various approaches to the validation of JRC-TIP products, including the comparison against in situ observations. We present a JRC-TIP processing system that satisfies all operational requirements and explain how it delivers stable climate data records. Since many aspects of JRC-TIP are generic, the package can serve as an example of a state-of-the-art system for retrieval of EO products, and this contribution can help the user to understand advantages and limitations of such products.

scholarly journals Consistent EO Land Surface Products including Uncertainty Estimates

2016 ◽  
Author(s):  
Thomas Kaminski ◽  
Bernard Pinty ◽  
Michael Voßbeck ◽  
Maciej Lopatka ◽  
Nadine Gobron ◽  
...  
Keyword(s):  
Land Surface ◽  
Prediction Models ◽  
Weather Prediction ◽  
Processing System ◽  
Research Centre ◽  
Soil System ◽  
Global Models ◽  
Area Index ◽  
Canopy Soil ◽  
Uncertainty Estimates

Abstract. Earth Observation (EO) land products have been demonstrated to provide a constraint on the terrestrial carbon cycle that is complementary to the record of atmospheric carbon dioxide. We present the Joint Research Centre Two-stream Inversion Package (JRC-TIP) for retrieval of variables characterising the state of the vegetation-soil system. The system provides a set of land surface variables that satisfy all requirements for assimilation into the land component of climate and numerical weather prediction models. Being based on a one dimensional representation of the radiative transfer within the canopy-soil system such as those used in the land surface components of advanced global models, the JRC-TIP products are not only physically consistent internally, but also achieve a high degree of consistency with these global models. Furthermore, the products are provided with full uncertainty information. We describe how these uncertainties are derived in a fully traceable manner without any hidden assumptions from the input observations, which are typically broadband white sky albedo products. Our discussion of the product uncertainty ranges, including the uncertainty reduction, highlights the central role of the leaf area index which describes the density of the canopy. We explain the generation of products aggregated to coarser spatial resolution than that of the native albedo input and describe various approaches to validation of JRC-TIP products, including the comparison against in-situ observations. We present a JRC-TIP processing system that satisfies all operational requirements and explain how it delivers stable climate data records. As many aspects of JRC-TIP are generic the package can serve as an example of a state-of-the-art system for retrieval of EO products, and this contribution can help the user to understand advantages and limitations of such products.

Surface Emissivity Impact on Temperature and Moisture Soundings from Hyperspectral Infrared Radiance Measurements

2011 ◽  
Vol 50 (6) ◽  
pp. 1225-1235 ◽  
Author(s):  
Zhigang Yao ◽  
Jun Li ◽  
Jinlong Li ◽  
Hong Zhang
Keyword(s):  
Land Surface ◽  
Prediction Models ◽  
Weather Prediction ◽  
Atmospheric Temperature ◽  
Retrieval Algorithm ◽  
Surface Emissivity ◽  
Angle Dependence ◽  
Infrared Radiance ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
The Impact

AbstractAn accurate land surface emissivity (LSE) is critical for the retrieval of atmospheric temperature and moisture profiles along with land surface temperature from hyperspectral infrared (IR) sounder radiances; it is also critical to assimilating IR radiances in numerical weather prediction models over land. To investigate the impact of different LSE datasets on Atmospheric Infrared Sounder (AIRS) sounding retrievals, experiments are conducted by using a one-dimensional variational (1DVAR) retrieval algorithm. Sounding retrievals using constant LSE, the LSE dataset from the Infrared Atmospheric Sounding Interferometer (IASI), and the baseline fit dataset from the Moderate Resolution Imaging Spectroradiometer (MODIS) are performed. AIRS observations over northern Africa on 1–7 January and 1–7 July 2007 are used in the experiments. From the limited regional comparisons presented here, it is revealed that the LSE from the IASI obtained the best agreement between the retrieval results and the ECMWF reanalysis, whereas the constant LSE gets the worst results when the emissivities are fixed in the retrieval process. The results also confirm that the simultaneous retrieval of atmospheric profile and surface parameters could reduce the dependence of soundings on the LSE choice and finally improve sounding accuracy when the emissivities are adjusted in the iterative retrieval. In addition, emissivity angle dependence is investigated with AIRS radiance measurements. The retrieved emissivity spectra from AIRS over the ocean reveal weak angle dependence, which is consistent with that from an ocean emissivity model. This result demonstrates the reliability of the 1DVAR simultaneous algorithm for emissivity retrieval from hyperspectral IR radiance measurements.

Impact of GVF Derivation Methods on Noah Land Surface Model Simulations and WRF Model Forecasts

2018 ◽  
Vol 19 (12) ◽  
pp. 1917-1933 ◽  
Author(s):  
Li Fang ◽  
Xiwu Zhan ◽  
Christopher R. Hain ◽  
Jifu Yin ◽  
Jicheng Liu
Keyword(s):  
Land Surface ◽  
Vegetation Index ◽  
Weather Prediction ◽  
Wrf Model ◽  
Land Surface Model ◽  
Surface Model ◽  
Area Index ◽  
Vegetation Fraction ◽  
Data Products ◽  
Noah Land Surface Model

Abstract Green vegetation fraction (GVF) plays a crucial role in the atmosphere–land water and energy exchanges. It is one of the essential parameters in the Noah land surface model (LSM) that serves as the land component of a number of operational numerical weather prediction models at the National Centers for Environmental Prediction (NCEP) of NOAA. The satellite GVF products used in NCEP models are derived from a simple linear conversion of either the normalized difference vegetation index (NDVI) from the Advanced Very High Resolution Radiometer (AVHRR) currently or the enhanced vegetation index (EVI) from the Visible Infrared Imaging Radiometer Suite (VIIRS) planned for the near future. Since the NDVI or EVI is a simple spectral index of vegetation cover, GVFs derived from them may lack the biophysical meaning required in the Noah LSM. Moreover, the NDVI- or EVI-based GVF data products may be systematically biased over densely vegetated regions resulting from the saturation issue associated with spectral vegetation indices. On the other hand, the GVF is physically related to the leaf area index (LAI), and thus it could be beneficial to derive GVF from LAI data products. In this paper, the EVI-based and the LAI-based GVF derivation methods are mathematically analyzed and are found to be significantly different from each other. Impacts of GVF differences on the Noah LSM simulations and on weather forecasts of the Weather Research and Forecasting (WRF) Model are further assessed. Results indicate that LAI-based GVF outperforms the EVI-based one when used in both the offline Noah LSM and WRF Model.

Estimation of the Minimum Canopy Resistance for Croplands and Grasslands Using Data from the 2002 International H2O Project

2008 ◽  
Vol 136 (11) ◽  
pp. 4452-4469 ◽  
Author(s):  
Joseph G. Alfieri ◽  
Dev Niyogi ◽  
Peter D. Blanken ◽  
Fei Chen ◽  
Margaret A. LeMone ◽  
...  
Keyword(s):  
Land Surface ◽  
Prediction Models ◽  
Weather Prediction ◽  
Moisture Flux ◽  
Mean Value ◽  
Surface Model ◽  
Canopy Resistance ◽  
Land Surface Models ◽  
Surface Models ◽  
The Mean

Abstract Vegetated surfaces, such as grasslands and croplands, constitute a significant portion of the earth’s surface and play an important role in land–atmosphere exchange processes. This study focuses on one important parameter used in describing the exchange of moisture from vegetated surfaces: the minimum canopy resistance (rcmin). This parameter is used in the Jarvis canopy resistance scheme that is incorporated into the Noah and many other land surface models. By using an inverted form of the Jarvis scheme, rcmin is determined from observational data collected during the 2002 International H2O Project (IHOP_2002). The results indicate that rcmin is highly variable both site to site and over diurnal and longer time scales. The mean value at the grassland sites in this study is 96 s m−1 while the mean value for the cropland (winter wheat) sites is one-fourth that value at 24 s m−1. The mean rcmin for all the sites is 72 s m−1 with a standard deviation of 39 s m−1. This variability is due to both the empirical nature of the Jarvis scheme and a combination of changing environmental conditions, such as plant physiology and plant species composition, that are not explicitly considered by the scheme. This variability in rcmin has important implications for land surface modeling where rcmin is often parameterized as a constant. For example, the Noah land surface model parameterizes rcmin for the grasslands and croplands types in this study as 40 s m−1. Tests with the coupled Weather Research and Forecasting (WRF)–Noah model indicate that the using the modified values of rcmin from this study improves the estimates of latent heat flux; the difference between the observed and modeled moisture flux decreased by 50% or more. While land surface models that estimate transpiration using Jarvis-type relationships may be improved by revising the rcmin values for grasslands and croplands, updating the rcmin will not fully account for the variability in rcmin observed in this study. As such, it may be necessary to replace the Jarvis scheme currently used in many land surface and numerical weather prediction models with a physiologically based estimate of the canopy resistance.

Vertical profiles of wind gust statistics from a regional reanalysis using multivariate extreme value theory

2020 ◽  
Author(s):  
Julian Steinheuer ◽  
Petra Friederichs
Keyword(s):  
Wind Speed ◽  
Land Surface ◽  
Prediction Models ◽  
Weather Prediction ◽  
Value Theory ◽  
Extreme Value ◽  
Horizontal Wind ◽  
Wind Gust ◽  
Post Processing ◽  
Regional Reanalysis

<div>Wind and gust statistics at the hub height of a wind turbine are important parameters for the planning in the renewable energy sector. However, reanalyses based on numerical weather prediction models typically only give estimates for wind gusts at the standard measurement height of 10 m above the land surface. We present here a statistical post-processing that gives a conditional distribution for hourly peak wind speeds as a function of height. The conditioning variables are provided by the regional reanalysis COSMO-REA6. The post-processing is developed on the basis of observations of the peak wind speed in five vertical layers between 10 m and 250 m of the Hamburg Weather Mast. The statistical post-processing is based on a censored generalized extreme value (cGEV) distribution with non-stationary parameters. To select the most meaningful variables we use a least absolute shrinkage and selection operator. The vertical variation of the cGEV parameters is approximated using Legendre polynomials, allowing gust prediction at any desired height within the training range. Furthermore, the Pickands dependence function is used to investigate dependencies between gusts at different heights. The main predictors are the 10 m gust diagnosis, the barotropic and baroclinic modes of absolute horizontal wind speed, the mean absolute horizontal wind in 700 hPa, the surface pressure tendency and the lifted index. Proper scores show improvements of up to 60 %, especially at higher vertical levels when compared to climatology. The post-processing model with a Legendre approximation is able to provide reliable predictions of gust statistics at unobserved intermediate levels. The strength of the dependence between the gusts at different levels is not stationary and strongly modulated by the vertical stability of the atmosphere.</div>

scholarly journals The Development and Evaluation of a Statistical–Dynamical Tropical Cyclone Genesis Guidance Tool

2016 ◽  
Vol 32 (1) ◽  
pp. 27-46 ◽  
Author(s):  
Daniel J. Halperin ◽  
Robert E. Hart ◽  
Henry E. Fuelberg ◽  
Joshua H. Cossuth
Keyword(s):  
Tropical Cyclone ◽  
Prediction Models ◽  
Weather Prediction ◽  
Multiscale Model ◽  
Global Model ◽  
Tropical Cyclone Genesis ◽  
Regression Equations ◽  
Global Models ◽  
Tropical Weather ◽  
Cyclone Genesis

Abstract The National Hurricane Center (NHC) has stated that guidance on tropical cyclone (TC) genesis is an operational forecast improvement need, particularly since numerical weather prediction models produce TC-like features and operationally required forecast lead times recently have increased. Using previously defined criteria for TC genesis in global models, this study bias corrects TC genesis forecasts from global models using multiple logistic regression. The derived regression equations provide 48- and 120-h probabilistic genesis forecasts for each TC genesis event that occurs in the Environment Canada Global Environmental Multiscale Model (CMC), the NCEP Global Forecast System (GFS), and the Met Office's global model (UKMET). Results show select global model output variables are good discriminators between successful and unsuccessful TC genesis forecasts. Independent verification of the regression-based probabilistic genesis forecasts during 2014 and 2015 are presented. Brier scores and reliability diagrams indicate that the forecasts generally are well calibrated and can be used as guidance for NHC’s Tropical Weather Outlook product. The regression-based TC genesis forecasts are available in real time online.

scholarly journals Vertical profiles of wind gust statistics from a regional reanalysis using multivariate extreme value theory

2019 ◽  
Author(s):  
Julian Steinheuer ◽  
Petra Friederichs
Keyword(s):  
Wind Speed ◽  
Land Surface ◽  
Prediction Models ◽  
Weather Prediction ◽  
Value Theory ◽  
Extreme Value ◽  
Horizontal Wind ◽  
Wind Gust ◽  
Post Processing ◽  
Regional Reanalysis

Abstract. Many applications require wind gust estimates at very different atmospheric height levels. For example, the renewable energy sector is interested in wind and gust predictions at the hub height of a wind power plant. However, numerical weather prediction models typically derive estimates for wind gusts at the standard measurement height of 10 m above the land surface only. Here, we present a statistical post-processing to derive a conditional distribution for hourly peak wind speed as a function of height. The conditioning variables are taken from the regional reanalysis COSMO-REA6. The post-processing is trained using peak wind speed observations at five vertical levels between 10 m and 250 m of the Hamburg Weather Mast. The statistical post-processing is based on a censored generalized extreme value (cGEV) distribution with non-stationary parameters. We use a least absolute shrinkage and selection operator to select the most informative variables. Vertical variations of the cGEV parameters are approximated using Legendre polynomials, such that predictions may be derived at any desired vertical height. Further, the Pickands dependence function is used to assess dependencies between gusts at different heights. The most important predictors are the 10 m gust diagnostic, the barotropic and the baroclinic mode of absolute horizontal wind speed, the mean absolute horizontal wind in 700 hPa, the surface pressure tendency, and the lifted index. Proper scores show improvements with respect to climatology of up to 60 % especially at higher vertical levels. The post-processing model with a Legendre approximation is able to provide reliable predictions of gusts statistics at non-observed intermediate levels. The strength of dependency between gusts at different levels is non-stationary and strongly modulated by the vertical stability of the atmosphere.

scholarly journals The S2M meteorological and snow cover reanalysis over the French mountainous areas, description and evaluation (1958–2020)

2021 ◽  
Author(s):  
Matthieu Vernay ◽  
Matthieu Lafaysse ◽  
Diego Monteiro ◽  
Pascal Hagenmuller ◽  
Rafife Nheili ◽  
...  
Keyword(s):  
Snow Cover ◽  
Land Surface ◽  
Prediction Models ◽  
Weather Prediction ◽  
Scientific Publications ◽  
Mountain Areas ◽  
Data Set ◽  
Meteorological Observations ◽  
Surface Scheme

Abstract. This work introduces the S2M (SAFRAN - SURFEX/ISBA-Crocus - MEPRA) meteorological and snow cover reanalysis in the French Alps, Pyrenees and Corsica, spanning the time period from 1958 to 2020. The simulations are made over elementary areas, referred to as massifs, designed to represent the main drivers of the spatial variability observed in mountain ranges (elevation, slope and aspect). The meteorological reanalysis is performed by the SAFRAN system, which combines information from numerical weather prediction models (ERA-40 reanalysis from 1958 to 2002, ARPEGE from 2002 to 2020) and the best possible set of available in-situ meteorological observations. SAFRAN outputs are used to drive the Crocus detailed snow cover model, which is part of the land surface scheme SURFEX/ISBA. This model chain provides simulations of the evolution of the snow cover, underlying ground, and the associated avalanche hazard using the MEPRA model. This contribution describes and discusses the main climatological characteristics (climatology, variability and trends), and the main limitations of this dataset. We provide a short overview of the scientific applications using this reanalysis in various scientific fields related to meteorological conditions and the snow cover in mountain areas. An evaluation of the skill of S2M is also displayed, in particular through comparison to 665 independent in-situ snow depth observations. Further, we describe the technical handling of this open access data set, available at this address: http://dx.doi.org/10.25326/37#v2020.2. Scientific publications using this dataset must mention in the acknowledgments: "The S2M data are provided by Météo-France - CNRS, CNRM Centre d’Etudes de la Neige, through AERIS" and refer to it as Vernay et al. (2020).

A Real-Time MODIS Vegetation Product for Land Surface and Numerical Weather Prediction Models

2014 ◽  
Vol 52 (3) ◽  
pp. 1772-1786 ◽  
Author(s):  
Jonathan L. Case ◽  
Frank J. LaFontaine ◽  
Jordan R. Bell ◽  
Gary J. Jedlovec ◽  
Sujay V. Kumar ◽  
...  
Keyword(s):  
Real Time ◽  
Numerical Weather Prediction ◽  
Land Surface ◽  
Prediction Models ◽  
Weather Prediction ◽  
Numerical Weather ◽  
Numerical Weather Prediction Models

Global Air–Sea Fluxes of Heat, Fresh Water, and Momentum: Energy Budget Closure and Unanswered Questions

2019 ◽  
Vol 11 (1) ◽  
pp. 227-248 ◽  
Author(s):  
Lisan Yu
Keyword(s):  
Surface Energy ◽  
Fresh Water ◽  
Prediction Models ◽  
Weather Prediction ◽  
Ocean Surface ◽  
Surface Radiation ◽  
Global Ocean ◽  
Personal Perspective ◽  
Near Surface ◽  
Water Budgets

The ocean interacts with the atmosphere via interfacial exchanges of momentum, heat (via radiation and convection), and fresh water (via evaporation and precipitation). These fluxes, or exchanges, constitute the ocean-surface energy and water budgets and define the ocean's role in Earth's climate and its variability on both short and long timescales. However, direct flux measurements are available only at limited locations. Air–sea fluxes are commonly estimated from bulk flux parameterization using flux-related near-surface meteorological variables (winds, sea and air temperatures, and humidity) that are available from buoys, ships, satellite remote sensing, numerical weather prediction models, and/or a combination of any of these sources. Uncertainties in parameterization-based flux estimates are large, and when they are integrated over the ocean basins, they cause a large imbalance in the global-ocean budgets. Despite the significant progress that has been made in quantifying surface fluxes in the past 30 years, achieving a global closure of ocean-surface energy and water budgets remains a challenge for flux products constructed from all data sources. This review provides a personal perspective on three questions: First, to what extent can time-series measurements from air–sea buoys be used as benchmarks for accuracy and reliability in the context of the budget closures? Second, what is the dominant source of uncertainties for surface flux products, the flux-related variables or the bulk flux algorithms? And third, given the coupling between the energy and water cycles, precipitation and surface radiation can act as twin budget constraints—are the community-standard precipitation and surface radiation products pairwise compatible?

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