A Review of the Use of Geostationary Satellite Observations in Regional-Scale Models for Short-term Cloud Forecasting

Kurzrock, Frederik; Cros, Sylvain; Ming, Fabrice Chane; Otkin, Jason A.; Hutt, Axel; Linguet, Laurent; Lajoie, Gilles; Potthast, Roland

doi:10.1127/metz/2018/0904

Cited by 26 publications

(33 citation statements)

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“…Nevertheless, as in many other proofs of concept for DA methods, only one weather situation has been considered so far in terms of solar irradiance. An evaluation over a period of more than a few days is rare in peer-reviewed literature (Kurzrock et al, 2018). Therefore, an 25 evaluation of the free forecasts for a total of 44 days (Table 1) is performed in this subsection in order to quantify the impact of DA on GHI forecasts more meaningfully.…”

Section: Free Forecast Evaluationmentioning

confidence: 99%

“…The assimilation of satellite-derived cloud information into LAMs can be categorised into either radiance, or cloud property retrieval assimilation methods. Kurzrock et al (2018) provide a review of geostationary meteorological satellite DA in LAMs and show that evaluations of the diverse meth-30 ods that focus on cloud analyses under tropical conditions are rare in peer-reviewed literature. Moreover, many methods make use of additional observations such as ground-based observations or radiosoundings in order to optimise the DA performance.…”

Section: Introductionmentioning

confidence: 99%

“…Cloud property DA methods on the other hand explicitly focus on retrievals in regions of cloud presence in order to directly influence and improve the cloud analyses. Several considerations from Kurzrock et al (2018) inform the methodology applied in this study. Firstly, it is clear that the use of ensembles has become increasingly favoured, demonstrated by the growing number 5 of hybrid DA methods.…”

Section: Introductionmentioning

confidence: 99%

“…While in radiance assimilation the historical approach is to assimilate only clear sky observations, avoid cloud-affected observations and gradually move towards all-sky assimilation (Kurzrock et al, 2018), an opposite strategy is followed in this work on cloud property assimilation. No clear sky retrievals are assimilated in the experiments in this study.…”

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confidence: 99%

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Evaluation of WRF-DART (ARW v.3.9.1.1 and DART manhattan release) multi-phase cloud water path assimilation for short-term solar irradiance forecasting in a tropical environment

Kurzrock¹,

Nguyen²,

Sauer³

et al. 2019

Preprint

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Abstract. Numerical weather prediction models tend to underestimate cloud presence and therefore often overestimate global horizontal irradiance (GHI). The assimilation of cloud water path (CWP) retrievals from geostationary satellites using an ensemble Kalman filter (EnKF) led to improved short-term GHI forecasts of the Weather Research and Forecasting (WRF) model in mid-latitudes in case studies. An evaluation of the method under tropical conditions and a quantification of this improvement for study periods of more than a few days is still missing. This paper focuses on the assimilation of CWP retrievals in three phases (ice, supercooled, and liquid) in a 6-hourly cycling procedure, and on the impact of this method on short-term forecasts of GHI for Reunion Island, a tropical island in the South-West Indian Ocean. The multi-layer gridded cloud properties of NASA Langley's Satellite ClOud and Radiation Property retrieval System (SatCORPS) are assimilated using the EnKF of the Data Assimilation Research Testbed (DART) manhattan release (revision 12002) and the advanced research WRF (ARW) v3.9.1.1. The ability of the method to improve cloud analyses and GHI forecasts is demonstrated and a comparison using independent radiosoundings shows a reduction of specific humidity bias in the WRF analyses, especially in the low and mid troposphere. Ground-based GHI observations at 12 sites on Reunion Island are used to quantify the impact of CWP DA. Over a total of 44 days during austral summer time, when averaged over all sites, CWP data assimilation has a positive impact on GHI forecasts for all lead times between 5 and 14 hours. Root Mean Squared Error and Mean Absolute Error are reduced by 4 % and 3 % respectively.

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Section: Free Forecast Evaluationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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confidence: 99%

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Evaluation of WRF-DART (ARW v.3.9.1.1 and DART manhattan release) multi-phase cloud water path assimilation for short-term solar irradiance forecasting in a tropical environment

Kurzrock¹,

Nguyen²,

Sauer³

et al. 2019

Preprint

Self Cite

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“…This paper focuses on the following forecasting challenge: improve the short-term forecast of solar irradiance using satellite data assimilation. The forecast skill is indeed strongly influenced by the accuracy of the cloud and thermodynamics analyses in the initial representations, which can improve significantly using satellite data assimilation [21].…”

Section: Introductionmentioning

confidence: 99%

3D-VAR Data Assimilation of SEVIRI Radiances for the Prediction of Solar Irradiance in Italy Using WRF Solar Mesoscale Model—Preliminary Results

et al. 2020

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Solar power generation is highly fluctuating due to its dependence on atmospheric conditions. The integration of this variable resource into the energy supply system requires reliable predictions of the expected power production as a basis for management and operation strategies. This is one of the goals of the Solar Cloud project, funded by the Italian Ministry of Economic Development (MISE)—to provide detailed forecasts of solar irradiance variables to operators and organizations operating in the solar energy industry. The Institute of Methodologies for Environmental Analysis of the National Research Council (IMAA-CNR), participating to the project, implemented an operational chain that provides forecasts of all the solar irradiance variables at high temporal and horizontal resolution using the numerical weather prediction Advanced Research Weather Research and Forecasting (WRF-ARW) Solar version 3.8.1 released by the National Center for Atmospheric Research (NCAR) in August 2016. With the aim of improving the forecast of solar irradiance, the three-dimensional (3D-Var) data assimilation was tested to assimilate radiances from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) aboard the Meteosat Second Generation (MSG) geostationary satellite into WRF Solar. To quantify the impact, the model output is compared against observational data. Hourly Global Horizontal Irradiance (GHI) is compared with ground-based observations from Regional Agency for the Protection of the Environment (ARPA) and with MSG Shortwave Solar Irradiance estimations, while WRF Solar cloud coverage is compared with Cloud Mask by MSG. A preliminary test has been performed in clear sky conditions to assess the capability of the model to reproduce the diurnal cycle of the solar irradiance. The statistical scores for clear sky conditions show a positive performance of the model with values comparable to the instrument uncertainty and a correlation of 0.995. For cloudy sky, the solar irradiance and the cloud cover are better simulated when the SEVIRI radiances are assimilated, especially in the short range of the simulation. For the cloud cover, the Mean Bias Error one hour after the assimilation time is reduced from 41.62 to 20.29 W/m2 when the assimilation is activated. Although only two case studies are considered here, the results indicate that the assimilation of SEVIRI radiance improves the performance of WRF Solar especially in the first 3 hour forecast.

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The use of satellite and surface observations for initializing clouds in the HARMONIE NWP model

Gregow

Lindfors

Veen

et al. 2020

Meteorological Applications

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The study tests and further develops a previously presented method for adjusting initial clouds in the HARMONIE mesoscale numerical weather prediction (NWP) model. The method uses satellite-observed cloud properties from the Nowcasting and very short range forecasting Satellite Application Facility (NWCSAF) together with ground-based synoptic stations (SYNOP) observations, adjusting the humidity profile of the model based on the available cloud information with the overall aim to improve the forecast, in particular as regards clouds and solar radiation. A reference setup of the HARMONIE model is used as a baseline against which the cloud initialization experiments are compared. All HARMONIE experiments are run for July 2016, with a model run starting at 06Z each day. The HARMONIE outputs are compared with cloud fraction and solar radiation observations from groundbased stations, while satellite cloud observations are also used for inspecting the behaviour of the forecasts. The performance of other generally used verification parameters is also studied. The results as regards clouds are encouraging. Symmetrical extremal dependence index (SEDI) skill scores show improvement in initial cloud conditions in 84% and 74% of the cases, when evaluated for cloud-free (0-1 octas) and cloudy conditions (7-8 octas), respectively. The improvement lasts 2-4 hr into the forecast. Other parameters, however, show somewhat degraded skill as compared with the reference model run, while for solar radiation, the cloud initialization scheme exhibits somewhat ambiguous results. The distribution of solar radiation values is improved for relatively sunny conditions, while at the other end of the distribution, the cloud initialization scheme produces too many cases with relatively strongly attenuated radiation.

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A Review of the Use of Geostationary Satellite Observations in Regional-Scale Models for Short-term Cloud Forecasting

Cited by 26 publications

References 0 publications

Evaluation of WRF-DART (ARW v.3.9.1.1 and DART manhattan release) multi-phase cloud water path assimilation for short-term solar irradiance forecasting in a tropical environment

Evaluation of WRF-DART (ARW v.3.9.1.1 and DART manhattan release) multi-phase cloud water path assimilation for short-term solar irradiance forecasting in a tropical environment

3D-VAR Data Assimilation of SEVIRI Radiances for the Prediction of Solar Irradiance in Italy Using WRF Solar Mesoscale Model—Preliminary Results

The use of satellite and surface observations for initializing clouds in the HARMONIE NWP model

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