Model evaluation by a cloud classification based on multi-sensor observations

Abstract. The detailed understanding of clouds and their macrophysical properties is crucial to reduce uncertainties of cloud feedbacks and related processes in current climate and weather prediction models. Comprehensive evaluation of cloud characteristics using observations is the first step towards any improvement. An advanced observational product was developed by the Cloudnet project. A multi-sensor synergy of active and passive remote-sensing instruments is used to generate a Target Classification providing detailed information about cloud phase and structure. Nevertheless, this valuable product is only available for observations and there is yet no comparable surrogate for models. Therefore, a new cloud classification algorithm is presented to calculate a comparable classification for models by using the temperature, dew point and all hydrometeor profiles. The study explains the algorithm and shows possible evaluation methods making use of the new synthetic cloud classification. For example, the statistics of the vertical cloud distribution as well as e.g. the accuracy of cloud forecasts can be investigated regarding different cloud types. The algorithm and methods are exemplarily tested on two months of operational weather forecast data of the COSMO-DE model and compared to a Cloudnet supersite in Germany. Additionally, the cloud classification is applied to Large Eddy Simulations with a similar resolution as of the observations showing detailed cloud structures.

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Towards operational postprocessing of cloud cover at MeteoSwiss

10.5194/egusphere-egu2020-17886 ◽

2020 ◽

Author(s):

Stephan Hemri ◽

Christoph Spirig ◽

Jonas Bhend ◽

Lionel Moret ◽

Mark Liniger

Keyword(s):

Cloud Cover ◽

Prediction Models ◽

Weather Prediction ◽

Weather Forecast ◽

Forecast Errors ◽

Observation Data ◽

Model Output ◽

Ensemble Forecasts ◽

Direct Model ◽

Ensemble Model Output Statistics

Over the last decades ensemble approaches have become state-of-the-art for the quantification of weather forecast uncertainty. Despite ongoing improvements, ensemble forecasts issued by numerical weather prediction models (NWPs) still tend to be biased and underdispersed. Statistical postprocessing has proven to be an appropriate tool to correct biases and underdispersion, and hence to improve forecast skill. Here we focus on multi-model postprocessing of cloud cover forecasts in Switzerland. In order to issue postprocessed forecasts at any point in space, ensemble model output statistics (EMOS) models are trained and verified against EUMETSAT CM SAF satellite data with a spatial resolution of around 2 km over Switzerland. Training with a minimal record length of the past 45 days of forecast and observation data already produced an EMOS model improving direct model output (DMO). Training on a 3 years record of the corresponding season further improved the performance. We evaluate how well postprocessing corrects the most severe forecast errors, like missing fog and low level stratus in winter. For such conditions, postprocessing of cloud cover benefits strongly from incorporating additional predictors into the postprocessing suite. A quasi-operational prototype has been set up and was used to explore meteogram-like visualizations of probabilistic cloud cover forecasts.

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Probabilistic fire-danger forecasting: A framework for week-two forecasts using statistical post-processing techniques and the Global ECMWF Fire Forecast System (GEFF)

Weather and Forecasting ◽

10.1175/waf-d-21-0075.1 ◽

2021 ◽

Author(s):

Rochelle P. Worsnop ◽

Michael Scheuerer ◽

Francesca Di Giuseppe ◽

Christopher Barnard ◽

Thomas M. Hamill ◽

...

Keyword(s):

Prediction Models ◽

Weather Prediction ◽

Weather Forecast ◽

Reanalysis Data ◽

Post Processing ◽

Medium Range Weather Forecast ◽

Skill Scores ◽

Probabilistic Forecasts ◽

Systematic Biases ◽

Processing Techniques

AbstractWildfire guidance two weeks ahead is needed for strategic planning of fire mitigation and suppression. However, fire forecasts driven by meteorological forecasts from numerical weather prediction models inherently suffer from systematic biases. This study uses several statistical-postprocessing methods to correct these biases and increase the skill of ensemble fire forecasts over the contiguous United States 8–14 days ahead. We train and validate the post-processing models on 20 years of European Centre for Medium-range Weather Forecast (ECMWF) reforecasts and ERA5 reanalysis data for 11 meteorological variables related to fire, such as surface temperature, wind speed, relative humidity, cloud cover, and precipitation. The calibrated variables are then input to the Global ECMWF Fire Forecast (GEFF) system to produce probabilistic forecasts of daily fire-indicators which characterize the relationships between fuels, weather, and topography. Skill scores show that the post-processed forecasts overall have greater positive skill at Days 8–14 relative to raw and climatological forecasts. It is shown that the post-processed forecasts are more reliable at predicting above- and below-normal probabilities of various fire indicators than the raw forecasts and that the greatest skill for Days 8–14 is achieved by aggregating forecast days together.

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Evaluating Convective Initiation in High-Resolution Numerical Weather Prediction Models Using GOES-16 Infrared Brightness Temperatures

Monthly Weather Review ◽

10.1175/mwr-d-20-0272.1 ◽

2021 ◽

Vol 149 (4) ◽

pp. 1153-1172

Author(s):

David S. Henderson ◽

Jason A. Otkin ◽

John R. Mecikalski

Keyword(s):

High Resolution ◽

Prediction Models ◽

Weather Prediction ◽

Cloud Microphysics ◽

Cloud Water ◽

Convective Initiation ◽

Model Simulations ◽

Brightness Temperatures ◽

Cloud Characteristics ◽

Verification Methods

AbstractThe evolution of model-based cloud-top brightness temperatures (BT) associated with convective initiation (CI) is assessed for three bulk cloud microphysics schemes in the Weather Research and Forecasting Model. Using a composite-based analysis, cloud objects derived from high-resolution (500 m) model simulations are compared to 5-min GOES-16 imagery for a case study day located near the Alabama–Mississippi border. Observed and simulated cloud characteristics for clouds reaching CI are examined by utilizing infrared BTs commonly used in satellite-based CI nowcasting methods. The results demonstrate the ability of object-based verification methods with satellite observations to evaluate the evolution of model cloud characteristics, and the BT comparison provides insight into a known issue of model simulations producing too many convective cells reaching CI. The timing of CI from the different microphysical schemes is dependent on the production of ice in the upper levels of the cloud, which typically occurs near the time of maximum cloud growth. In particular, large differences in precipitation formation drive differences in the amount of cloud water able to reach upper layers of the cloud, which impacts cloud-top glaciation. Larger cloud mixing ratios are found in clouds with sustained growth leading to more cloud water lofted to the upper levels of the cloud and the formation of ice. Clouds unable to sustain growth lack the necessary cloud water needed to form ice and grow into cumulonimbus. Clouds with slower growth rates display similar BT trends as clouds exhibiting growth, which suggests that forecasting CI using geostationary satellites might require additional information beyond those derived at cloud top.

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Calibration of solar radiation ensemble forecasts using convolutional neural network

10.5194/egusphere-egu21-7359 ◽

2021 ◽

Author(s):

Florian Dupuy ◽

Yen-Sen Lu ◽

Garrett Good ◽

Michaël Zamo

Keyword(s):

Neural Network ◽

Solar Radiation ◽

Convolutional Neural Network ◽

Prediction Models ◽

Weather Prediction ◽

Special Kind ◽

Ground Truth ◽

Weather Forecast ◽

Ensemble Forecasts ◽

Processing Techniques

Ensemble forecast approaches have become state-of-the-art for the quantification of weather forecast uncertainty. However, ensemble forecasts from numerical weather prediction models (NWPs) still tend to be biased and underdispersed, hence justifying the use of statistical post-processing techniques to improve forecast skill. In this study, ensemble forecasts are post-processed using a convolutional neural network (CNN). CNNs are the most popular machine learning tool to deal with images. In our case, CNNs allow to integrate information from spatial patterns contained in NWP outputs.We focus on solar radiation forecasts for 48 hours ahead over Europe from the 35-members ARPEGE (M&#233;t&#233;o-France global NWP) and a 512-members WRF (Weather Research and Forecasting) ensembles. We used a U-Net (a special kind of CNN) designed to produce a probabilistic forecast (quantiles) using as ground truth the CAMS (Copernicus Atmosphere Monitoring System) radiation service dataset with a spatial resolution of 0.2&#176;.

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Precipitation Nowcasting using Deep Neural Network

10.5194/egusphere-egu2020-21631 ◽

2020 ◽

Author(s):

Mohamed Chafik Bakey ◽

Mathieu Serrurier

Keyword(s):

Traffic Management ◽

Prediction Models ◽

Short Term Memory ◽

Weather Prediction ◽

Weather Forecast ◽

Proposed Model ◽

Anticipation Time ◽

Memory Network ◽

Convolutional Encoder

Precipitation nowcasting is the prediction of the future precipitation rate in a given geographical region with an anticipation time of a few hours at most. It is of great importance for weather forecast users, for activitites ranging from outdoor activities and sports competitions to airport traffic management. In contrast to long-term precipitation forecasts which are traditionally obtained from numerical weather prediction models, precipitation nowcasting needs to be very fast. It is therefore more challenging to obtain because of this time constraint. Recently, many machine learning based methods had been proposed. In this work, we develop an original deep learning approach. We formulate precipitation nowcasting issue as a video prediction problem where both input and prediction target are image sequences. The proposed model combines a Long Short-Term Memory network (LSTM) with a convolutional encoder-decoder network (U-net). Experiments show that our method captures spatiotemporal correlations and yields meaningful forecasts

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The impact of SST on the weather forecast quality in the Bulgarian Antarctic Base area on Livingstone Island

10.5194/egusphere-egu2020-9347 ◽

2020 ◽

Author(s):

Boriana Chtirkova ◽

Elisaveta Peneva

Keyword(s):

Prediction Models ◽

Weather Prediction ◽

Wrf Model ◽

Weather Forecast ◽

Horizontal Resolution ◽

Initial Time ◽

Test Cases ◽

Validation Data ◽

The Impact ◽

Forecast Quality

The weather forecast of good quality is essential for the humans living and operating in the Bulgarian Antarctic Base. The numerical weather prediction models in southern high latitude regions still need improvement as the user community is limited, little test cases are documented and validation data are scarce. Not lastly, the challenge of distributing the output results under poor internet conditions has to be addressed.The Bulgarian Antarctic Base (BAB) is located on the Livingstone Island coast at 62&#8304;S and 60&#8304;W. The influence of the Southern ocean is significant, thus important to be correctly taken into account in the numerical forecast. The modeling system is based on the WRF model, configured in three nested domains down to 1 km horizontal resolution, centered in BAB. The main objective of the study is to quantify the Sea Surface Temperature (SST) impact and to recommend the frequency and way to perform measurements of the SST near the base. The focus is on prediction of right initial time and period of &#8220;bad&#8221; weather events like storms, frontal zones, and severe winds. Several test cases are considered with available measurements of temperature, pressure and wind speed in BAB during the summer season in 2017. The numerical 3 days forecast is performed and the model skill to capture the basic meteorological events in this period is discussed. Sensitivity experiments to SST values in the nearby marine area are concluded and the SST influence on the model forecast quality is analyzed.

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Overview of the first HyMeX Special Observation Period over Italy: observations and model results

Hydrology and Earth System Sciences ◽

10.5194/hess-18-1953-2014 ◽

2014 ◽

Vol 18 (5) ◽

pp. 1953-1977 ◽

Cited By ~ 40

Author(s):

R. Ferretti ◽

E. Pichelli ◽

S. Gentile ◽

I. Maiello ◽

D. Cimini ◽

...

Keyword(s):

Prediction Models ◽

Hydrological Cycle ◽

Initial Conditions ◽

Weather Prediction ◽

Central Italy ◽

Weather Forecast ◽

Western Mediterranean ◽

Target Area ◽

Northeastern Italy ◽

Observation Period

Abstract. The Special Observation Period (SOP1), part of the HyMeX campaign (Hydrological cycle in the Mediterranean Experiments, 5 September–6 November 2012), was dedicated to heavy precipitation events and flash floods in the western Mediterranean, and three Italian hydro-meteorological monitoring sites were identified: Liguria–Tuscany, northeastern Italy and central Italy. The extraordinary deployment of advanced instrumentation, including instrumented aircrafts, and the use of several different operational weather forecast models, including hydrological models and marine models, allowed an unprecedented monitoring and analysis of high-impact weather events around the Italian hydro-meteorological sites. This activity has seen strong collaboration between the Italian scientific and operational communities. In this paper an overview of the Italian organization during SOP1 is provided, and selected Intensive Observation Periods (IOPs) are described. A significant event for each Italian target area is chosen for this analysis: IOP2 (12–13 September 2012) in northeastern Italy, IOP13 (15–16 October 2012) in central Italy and IOP19 (3–5 November 2012) in Liguria and Tuscany. For each IOP the meteorological characteristics, together with special observations and weather forecasts, are analyzed with the aim of highlighting strengths and weaknesses of the forecast modeling systems, including the hydrological impacts. The usefulness of having different weather forecast operational chains characterized by different numerical weather prediction models and/or different model set up or initial conditions is finally shown for one of the events (IOP19).

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Exploitation of a New Short-Term Multimodel Photovoltaic Power Forecasting Method in the Very Short-Term Horizon to Derive a Multi-Time Scale Forecasting System

Energies ◽

10.3390/en14030789 ◽

2021 ◽

Vol 14 (3) ◽

pp. 789

Author(s):

Elena Collino ◽

Dario Ronzio

Keyword(s):

Prediction Models ◽

Moving Average ◽

Weather Prediction ◽

Weather Forecast ◽

Small Scale ◽

Multiple Time ◽

Short Term ◽

Production Forecasting ◽

Moving Average Model ◽

Power Forecasting

The relentless spread of photovoltaic production drives searches of smart approaches to mitigate unbalances in power demand and supply, instability on the grid and ensuring stable revenues to the producer. Because of the development of energy markets with multiple time sessions, there is a growing need of power forecasting for multiple time steps, from fifteen minutes up to days ahead. To address this issue, in this study both a short-term-horizon of three days and a very-short-term-horizon of three hours photovoltaic production forecasting methods are presented. The short-term is based on a multimodel approach and referred to several configurations of the Analog Ensemble method, using the weather forecast of four numerical weather prediction models. The very-short-term consists of an Auto-Regressive Integrated Moving Average Model with eXogenous input (ARIMAX) that uses the short-term power forecast and the irradiance from satellite elaborations as exogenous variables. The methods, applied for one year to four small-scale grid-connected plants in Italy, have obtained promising improvements with respect to refence methods. The time horizon after which the short-term was able to outperform the very-short-term has also been analyzed. The study also revealed the usefulness of satellite data on cloudiness to properly interpret the results of the performance analysis.

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On the Use of QuikSCAT Scatterometer Measurements of Surface Winds for Marine Weather Prediction

Monthly Weather Review ◽

10.1175/mwr3179.1 ◽

2006 ◽

Vol 134 (8) ◽

pp. 2055-2071 ◽

Cited By ~ 89

Author(s):

Dudley B. Chelton ◽

Michael H. Freilich ◽

Joseph M. Sienkiewicz ◽

Joan M. Von Ahn

Keyword(s):

North Atlantic ◽

Gulf Stream ◽

Prediction Models ◽

Numerical Models ◽

Weather Prediction ◽

Processing System ◽

Weather Forecast ◽

Surface Winds ◽

Ocean Prediction ◽

Numerical Weather Prediction Models

Abstract The value of Quick Scatterometer (QuikSCAT) measurements of 10-m ocean vector winds for marine weather prediction is investigated from two Northern Hemisphere case studies. The first of these focuses on an intense cyclone with hurricane-force winds that occurred over the extratropical western North Pacific on 10 January 2005. The second is a 17 February 2005 example that is typical of sea surface temperature influence on low-level winds in moderate wind conditions in the vicinity of the Gulf Stream in the western North Atlantic. In both cases, the analyses of 10-m winds from the NCEP and ECMWF global numerical weather prediction models considerably underestimated the spatial variability of the wind field on scales smaller than 1000 km compared with the structure determined from QuikSCAT observations. The NCEP and ECMWF models both assimilate QuikSCAT observations. While the accuracies of the 10-m wind analyses from these models measurably improved after implementation of the QuikSCAT data assimilation, the information content in the QuikSCAT data is underutilized by the numerical models. QuikSCAT data are available in near–real time in the NOAA/NCEP Advanced Weather Interactive Processing System (N-AWIPS) and are used extensively in manual analyses of surface winds. The high resolution of the QuikSCAT data is routinely utilized by forecasters at the NOAA/NCEP Ocean Prediction Center, Tropical Prediction Center, and other NOAA weather forecast offices to improve the accuracies of wind warnings in marine forecasts.

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Radio Occultation Observations as Anchor Observations in Numerical Weather Prediction Models and Associated Reduction of Bias Corrections in Microwave and Infrared Satellite Observations

Journal of Atmospheric and Oceanic Technology ◽

10.1175/jtech-d-13-00059.1 ◽

2014 ◽

Vol 31 (1) ◽

pp. 20-32 ◽

Cited By ~ 25

Author(s):

L. Cucurull ◽

R. A. Anthes ◽

L.-L. Tsao

Keyword(s):

Data Assimilation ◽

Numerical Weather Prediction ◽

Radio Occultation ◽

Prediction Models ◽

Positive Impact ◽

Weather Prediction ◽

Weather Forecast ◽

Operational Environment ◽

Numerical Weather ◽

Bias Corrections

Abstract Satellite radiance measurements are used daily at numerical weather prediction (NWP) centers around the world, providing a significant positive impact on weather forecast skill. Owing to the existence of systematic errors, either in the observations, instruments, and/or forward models, which can be larger than the signal, the use of infrared or microwave radiances in data assimilation systems requires significant bias corrections. As most bias-correction schemes do not correct for biases that exist in the model forecasts, the model needs to be grounded by an unbiased observing system. These reference measurements, also known as “anchor observations,” prevent a drift of the model to its own climatology and associated biases, thus avoiding a spurious drift of the observation bias corrections. This paper shows that the assimilation of global positioning system (GPS) radio occultation (RO) observations over a 3-month period in an operational NWP system results in smaller, more accurate bias corrections in infrared and microwave observations, resulting in an overall more effective use of satellite radiances and a larger number of radiance observations that pass quality control. A full version of the NCEP data assimilation system is used to evaluate the results on the bias corrections for the High Resolution Infrared Radiation Sounder-3 (HIRS-3) on NOAA-17 and the Advanced Microwave Sounding Unit-A (AMSU-A) on NOAA-15 in an operational environment.

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