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opencc-by-4.0Dec 2021View details →

Data of manuscript "Forecasting day-ahead 1-minute irradiance variability from Numerical Weather Predictions" submitted to Solar Energy

This is the data corresponding to manuscript "Forecasting day-ahead 1-minute irradiance variability from Numerical Weather Predictions" by Kreuwel et al., 2022, submitted to Solar Energy.   The file `basic_stats.tar.gz` contains a broad set of standard statistics of surface meteorology and vertical profiles. The file `sw_flux_dn_xy.tar.gz` contains spatial cross sections of downwelling shortwave radiation.

opencc-zeroMay 2024View details →

Can ingredients based forecasting be learned? Disentangling a random forest's severe weather predictions

Machine learning (ML)-based models have been rapidly integrated into forecast practices across the weather forecasting community in recent years. While ML tools introduce additional data to forecasting operations, there is a need for explainability to be available alongside the model output, such that the guidance can be transparent and trustworthy for the forecaster. This work makes use of the algorithm tree interpreter (TI) to disaggregate the contributions of meteorological features used in the Colorado State University Machine Learning Probabilities (CSU-MLP) system, a random forest-based ML tool that produces real-time probabilistic forecasts for severe weather using inputs from the Global Ensemble Forecast System v12. TI feature contributions are analyzed in time and space for CSU-MLP day-2 and 3 individual hazard (tornado, wind, and hail) forecasts and day-4 aggregate severe forecasts over a 2-yr period. For individual forecast periods, this work demonstrates that feature contributions derived from TI can be interpreted in an ingredients-based sense, effectively making the CSU-MLP probabilities physically interpretable. When investigated in an aggregate sense, TI illustrates that the CSU-MLP system's predictions use meteorological inputs in ways that are consistent with the spatiotemporal patterns seen in meteorological fields that pertain to severe storms climatology. This work concludes with a discussion on how these insights could be beneficial for model development, real-time forecast operations, and retrospective event analysis.

opencc-by-4.0Jun 2024View details →

Weather data (forecast and observation) at 48 locations in France for beginning of 2024 for Machine Learning Training

The data provided data are historical weather measurement and forecast at 48 locations in France and its boundary. Measurements are inside files named MES_YYYY.csv with YYYY is the id code of the station. The file "Station_list.csv" contains the list of the 45 locations with the id code, the name and then the latitude and longitude. Forecasts are inside files named XXX_YYYY.csv with YYYY the id code corresponding of the location of the grid ouput close to the associated observation location. XXX is the id of the numerical forecast:     "GFS0.25-Complet" for GFS file at 0.25° resolution     "LEXIS" for WRF produced by EVEREST project using the LEXIS chain     "WRF3KM-Complet" for WRF at 3km resolution produced by NUMTECH     "WRF12KM-Complet" for WRF at 12km resolution produced by NUMTECH Description of MES-YYYY files: - One line per measurement with hourly resolution - columns are: Date(TU),Temperature2m_degC,WindSpeed10m_m/s,WindDirection10m_m/s     Date = date of measurement in TU and format DD/MM/YYYY HH:MM     Temperature2m_degC = air temperature at 2m height in °Celsius     WindSpeed10m_m/s = wind speed at 10m height in m/s     WindDirection10m_deg = wind direction at 10m height in deg. (0 or 360 = wind from north to south, 45°=wind from east to east, ....) If measurement is not available for a specific hour for one parameter, the value "-999" is used. The observation data gocfrom 28/01/2024 00HTU to 17/03/2024 23HTU Description of XXX_YYYY forecast files: - One line per forecast with hourly resolution - columns are: First date run (TU),Forecast date,Temperature2m_degC,WindSpeed10m_m/s,WindDirection10m_m/s     First date run (TU) = date of start of the forecast in TU and format DD/MM/YYYY HH:MM. HH could be 00 and 12 according to the cycle of forecast start.     Forecast date = date of the forecast in TU and format DD/MM/YYYY HH:MM. HH go from 00 to 23.      Temperature2m_degC = air temperature at 2m height in °Celsius     WindSpeed10m_m/s = wind speed at 10m height in m/s     WindDirection10m_deg = wind direction at 10m height in deg. (0 or 360 = wind from north to south, 45°=wind from east to east, ....) If forecast is not available for a specific hour for one parameter, the value "-999" is used. The forecast data go from 28/01/2024 00HTU to 17/03/2024 23HTU

opencc-by-4.0Jun 2019View details →

Videos for "Weather and climate forecasting with neural networks: using GCMs with different complexity as study-ground"

Supplementary videos for the paper "Weather and climate forecasting with neural networks: using GCMs with different complexity as study-ground" by S. Scher and G. Messori, Geoscientific Model Development 2019

opencc-by-4.0Aug 2024View details →

Supporting data ocean model GMD submission: From Weather Data to River Runoff: Leveraging Spatiotemporal Convolutional Networks for Comprehensive Discharge Forecasting

Ocean model salinity data used for the comparison of the ConvLSTM river runoff model and the original E-HYPE based model simulations.

opencc-by-4.0Apr 2021View details →

Model output data for compressible EULAG dynamical core in COSMO: convective-scale Alpine weather forecasts

The archive contains model output data for article "Compressible EULAG dynamical core in COSMO: convective-scale Alpine weather forecasts" to be published in Monthly Weather Review. The article presents the semi-implicit compressible EULAGas a newdynamical core for convective-scale numerical weather prediction. The core is implemented within the infrastructure of the operational model of the Consortium for Small Scale Modeling (COSMO), forming the NWP COSMO-EULAG model (CE). This regional high-resolution implementation of the dynamical core complements its global implementation in the Finite-Volume Module of ECMWF’s Integrated Forecasting System. The paper documents the first operational-like application of the dynamical core for realistic weather forecasts. After discussing the formulation of the core and its coupling with the host model, the paper considers several high-resolution prognostic experiments over complex Alpine orography. Standard verification experiments examine the sensitivity of the CE forecast to the choice of the advection routine and assess the forecast skills against those of the default COSMO Runge-Kutta dynamical core at the 2.2 km grid showing a general improvement. The skills are also compared using satellite observations for a weak-flow convective Alpine weather case-study, showing favorable results. Additional validation of the new CE framework for partly convection-resolving forecasts using 1.1 km, 0.55 km, 0.22 km, and 0.1 km grids, designed to challenge its numerics and test the dynamics-physics coupling, demonstrates its high robustness in simulating multi-phase flows over complex mountain terrain, with slopes reaching 85 degrees, and the flow’s realistic representation.

opencc-zeroDec 2022View details →

Data for: A new paradigm for medium-range severe weather forecasts: Probabilistic random forest-based predictions

Historical observations of severe weather and simulated severe weather environments (i.e., features) from the Global Ensemble Forecast System v12 (GEFSv12) Reforecast Dataset (GEFS/R) are used in conjunction to train and test random forest (RF) machine learning (ML) models to probabilistically forecast severe weather out to days 4–8. RFs are trained with ~9 years of the GEFS/R and severe weather reports to establish statistical relationships. Feature engineering is briefly explored to examine alternative methods for gathering features around observed events, including simplifying features using spatial averaging and increasing the GEFS/R ensemble size with time-lagging. Validated RF models are tested with ~1.5 years of real-time forecast output from the operational GEFSv12 ensemble and are evaluated alongside expert human-generated outlooks from the Storm Prediction Center (SPC). Both RF-based forecasts and SPC outlooks are skillful with respect to climatology at days 4 and 5 with diminishing skill thereafter. The RF-based forecasts exhibit tendencies to slightly underforecast severe weather events, but they tend to be well-calibrated at lower probability thresholds. Spatially averaging predictors during RF training allows for prior-day thermodynamic and kinematic environments to generate skillful forecasts, while time-lagging acts to expand the forecast areas, increasing resolution but decreasing overall skill. The results highlight the utility of ML-generated products to aid SPC forecast operations into the medium range.

publicMay 2024View details →

Can ingredients based forecasting be learned? Disentangling a random forest's severe weather predictions

Open the record for dataset details and reuse information.

publicJan 2023View details →

Data for: A new paradigm for medium-range severe weather forecasts: Probabilistic random forest-based predictions

Open the record for dataset details and reuse information.

Discussion Survey from Chapman Conference on Scientific Challenges Pertaining to Space Weather Forecasting Including Extremes

These files include summaries of pre-meeting survey of  important topics to discuss at the Chapman Conference on Scientific Challenges Pertaining to Space Weather Forecasting Including Extremes The Chapman Conference was supported by NASA Grants: 936723.02.01.09.14 and 936723.02.01.11.21 and by NSF Award AGS 1848885

Questions for Presenters at the Chapman Conference on Scientific Challenges Pertaining to Space Weather Forecasting Including Extremes

This file lists the questions asked of  Chapman Conference presenters by the Chapman Conference attendees. -Questions, along with presenter names and presentation titles are included -The document contains        1) an explanation sheet          2) a sheet with all questions sorted by day and presenter         3) a sheet with questions that were asked in a general setting. The materials have been lightly edited to spell out acronyms, correct spelling and clarify non specific references when possible. The Chapman Conference on Scientific Challenges Pertaining to Space Weather Forecasting Including Extremes was supported by NASA Grants 936723.02.01.09.14 and 936723.02.01.11.21 and by  NSF Award AGS 1848885

Priorities Survey from the Chapman Conference on Scientific Challenges Pertaining to Space Weather Forecasting Including Extremes

Mid-meeting survey results on Priorities from the Chapman Conference on Scientific Challenges Pertaining to Space Weather Forecasting Including Extremes 2 Files are provided:  An Excel Spreadsheet and a summary pdf of highest priorities. Statistics compiled by Tomoko Matsuo The Chapman Conference was supported by NSF Award AGS 1848885 and NASA grants  936723.02.01.09.14 and  936723.02.01.11.21

Discussion Notes from Chapman Conference on Scientific Challenges Pertaining to Space Weather Forecasting Including Extremes

Discussion notes from the Chapman Conference on Scientific Challenges Pertaining to Space Weather Forecasting Including Extremes Notes from Days 1, 3 and 4 are provided.  Conference Day 2 was a 'Poster Day' These notes were aggregated from meeting scribes and conveners The Chapman Conference was supported by NSF Award AGS 1848885 and NASA grants  936723.02.01.09.14 and  936723.02.01.11.21

Table of Contents for Meeting Artifacts from Chapman Conference on Scientific Challenges Pertaining to Space Weather Forecasting Including Extremes

Table of Contents of Meeting Artifacts and Output from the Chapman Conference on Scientific Challenges Pertaining to Space Weather Forecasting Including Extremes, 11-15 February 2019, Pasadena, CA, USA Each entry in the table of contents provides a short description and/or artifact title, along with the number of associated files and a weblink showing the associated DOI or permanent URL. The Chapman Conference was supported by NSF Award AGS 1848885 and NASA grants  936723.02.01.09.14 and  936723.02.01.11.21

opencc-by-nc-sa-4.0Apr 2024View details →

Extreme Weather Event Real-time Attribution Machine (EWERAM) forecasts for Cyclone Gabrielle

These files contain the hourly precipitation, wind, humidity, and pressure data as well as the land-sea mask, orography, and regional council data supporting the investigation into the human role in Cyclone Gabrielle performed by the EWERAM (Extreme Weather Event Real-time Attribution Machine) consortium.  The EWERAM experiment design is outline by Tradowsky and co-authors (2023, 10.1088/2752-5295/acf4b4). Directory and data format follows the conventions of the Climate of the 20th Century Plus Detection and Attribution (C20C+ D&A) Project.  Details are provided at https://portal.nersc.gov/c20c/experiment.html and in Stone and co-authors (2019, 10.1016/j.wace.2019.100206).

opencc-by-4.0Apr 2024View details →

Data from: The Jive Verification System and its Transformative Impact on Weather Forecasting Operations

This repository contains the data used for the analysis in the paper “The Jive Verification System and its Transformative Impact on Weather Forecasting Operations” Loveday et al. 2024 to be submitted to the Bulletin of the American Meteorological Society (BAMS).

opencc-by-4.0Nov 2021View details →

Annual Weather with Forecast

Dataset for 2021 of weather in different locations.

opencc-by-4.0Jan 2022View details →

Predicting the execution time of COSMO weather forecast models

This data set is the work of E. Di Giacomo Master Thesis at the University of Bologna. Predicting the execution time of a numerical weather forecast model is a complex task. Generally, these models simulate the evolution of atmospheric weather and they are typically used for the production of weather forecasts, one or multiple times a day. Given their computational complexity, they require large computing capabilities, such as High Performance Computing systems. In these systems, job scheduling and resource allocation are carefully managed to optimize the usage of the finite and expensive hardware resources; in particular, several allocation and related pricing decisions are based on estimates of the duration of the application submitted, such as the execution time of weather forecast models. A reliable prediction of execution time allows for a better management of the overall system, an improved planning of the model execution, as well as the identification of possible anomalies during the execution, thus providing great benefits to both system administrators and users. This data set regards a particular weather forecast model, namely the COSMO model, the weather forecasting model used at the the Hydro-Meteo-Climate Structure of Arpae Emilia-Romagna. The data set contains many execution times of the COSMO meteorological model run under a variety of different scientific parameters and parallelization levels.

opencc-by-4.0Jan 2022View details →

Code for JAMES publication - Implementation of prognostic cloud ice number concentrations for the Weather Research and Forecasting (WRF) Double-Moment 6-class (WDM6) microphysics scheme.

In this repository, we include the source codes for WRF microphysics parameterization used in the JAMES publication "Implementation of prognostic cloud ice number concentrations for the Weather Research and Forecasting (WRF) Double-Moment 6-class (WDM6) microphysics scheme"   The revised WDM6 code, which predicts the number concentration of cloud ice, and the original WDM6 code are uploaded. Each code is modified so that detailed microphysical processes can be found in wrfout.   Namelist files shows the namelist.input for each case.   Scripts file for figures in this manuscript are uploaded