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24 results for “Seasonal Forecast”
Data for 'Improved predictability of the Indian Ocean Dipole using seasonally modulated ENSO forcing forecasts'
<p>Abstract of the associated paper: Despite recent progress in seasonal forecast development, the predictive skill for the Indian Ocean Dipole (IOD) remains typically limited to a lead time of one season or less in both dynamical and empirical models. Here we develop a simple stochastic-dynamical model (SDM) to predict the IOD using seasonally modulated El Niño-Southern Oscillation (ENSO) forcing together with a seasonal modulation of the Indian Ocean coupled ocean-atmosphere feedback. The SDM, with either observed or forecasted ENSO forcing, exhibits generally higher skill and longer lead times for predicting IOD events than the operational Climate Forecast System Version 2 and the SINTEX system. These results affirm our hypothesis that operational IOD predictability beyond persistence is largely controlled by ENSO predictability and the signal-to-noise ratio of the system. Therefore, potential future ENSO improvements in models should also translate to more skillful IOD predictions.</p>
Data from: Comparing winter versus summer deepwater dissolved oxygen depletion with the potential for cross-seasonal forecasting of deepwater oxygen availability
<p>Depletion of deepwater dissolved oxygen (DO) in lakes has become increasingly prevalent and severe due to many external stressors, potentially threatening human-derived ecosystem services ranging from drinking water quality to fisheries. Using year-round, high-frequency DO data from 12 dimictic lakes, we compared three measures of deepwater DO depletion during winter and summer: DO depletion rate, DO minimum, and hypoxia duration. Hypoxia (DO < 3 mg L<sup>-1</sup>) occurred in over half of the lakes and persisted an average of 83% longer in summer than in winter. While we found no difference in DO depletion rates between winter versus summer, these rates were strongly related to lake morphology in winter but water transparency and temperature in summer. Winter hypoxia duration was negatively related to summer hypoxia duration, suggesting potential utility for forecasting DO depletion in the subsequent summer. Spring mixing efficacy was strongly related to winter minimum DO saturation and hypoxia duration, and was also a strong predictor of summer minimum DO saturation and hypoxia duration. Hence, these cross-seasonal patterns suggest deepwater DO metrics can be used to forecast DO availability in subsequent seasons, modified by the relative importance of morphology, water transparency, and temperature. These findings can allow for improved, early management when DO is predicted to be critically low based on previous seasons’ DO measurements, which can work to minimize the negative consequences for water quality and fisheries health associated with severe DO depletion.</p>
Seasonal forecasts of ocean heat content in ECMWF-SEAS5 and CMCC-SPS3
<p>Seasonal forecasts of ocean heat content in the upper 300m from two Copernicus Climate Change Service Systems: ECMWF SEAS5 and CMCC SPS3. Data was used for the following study:</p> <p>McAdam, R., Masina, S., Balmaseda, M. <em>et al.</em> Seasonal forecast skill of upper-ocean heat content in coupled high-resolution systems. <em>Clim Dyn</em> 58, 3335–3350 (2022). https://doi.org/10.1007/s00382-021-06101-3</p>
SEAS5/System4-LARSIM_ME Seasonal Streamflow Forecasts for German Waterways
<p>The datasets provided were produced as part of the IMPREX project for work package 4, task 1 “<em>Development of the regional and European scale reforecast dataset of hydrological extremes</em> “, work package 4, task 2 “<em>Analysis of the impact of changes in precipitation attributes from short to medium and climatic ranges</em>” and work package 9, task 3 “<em>Case studies</em>”. Analysis of the datasets are published in Meißner et al. 2017, in Deliverable 4.2 „ <em>The sensitivity of sub-seasonal to seasonal streamflow forecasts to meteorological forcing quality, modelled hydrology and the initial hydrological conditions</em> “ (Arnal et al. 2017) and in Deliverable 4.3 “<em>Forecast skill developments</em>” (Weerts et al. 2019). The aim was to evaluate the potential skill of seasonal streamflow forecasting for the German waterways Rhine, Elbe and Danube.</p> <p>As seasonal meteorological forecast data the reforecast dataset from ECMWF’s Seasonal Forecast System 4 (S4 hereafter) (Molteni et al. 2011) as well as from the fifth generation of ECMWF’s Seasonal forecasting system SEAS5 (ECMWF 2017, Johnson et al. 2018, Owens & Hewson 2018) of the period 1981 – 2016 were used.</p> <p>The horizontal resolution of System 4 is approximately 80 km. In operational mode the ensemble consists of 51 members generated by using an ensemble of initial conditions and by the use of stochastic physics. Re-forecasts starting on the 1st of each month for the years 1981-2016 are generated with the same model as used for the operational forecast. For the period 1981 – 2011 the ensemble size varies between 15 members (initialization months January, March, April, June, July, September, October, December) and 51 members (for the remaining months). Since 2012, the ensemble size is 51 members all over the year (operational forecasts).</p> <p>The fifth generation of ECMWF seasonal forecasting system SEAS5 replaced System 4 in November 2017. The horizontal resolution of the model is 0.4°x0.4° (approx. 36 km). The ensemble consists of 51-members created using a combination of Sea Surface Temperature SST and atmospheric initial condition perturbations and the activation of stochastic physics (ECMWF 2017). Re-Forecasts with the ensemble size of 25 members starting on the 1st of each month for the years 1981-2016 are generated with the same model as used for the operational forecast.</p> <p>The hydrological model applied is called LARSIM-ME (ME – MittelEuropa = Central Europe) and is based in the model software LARSIM (Large Area Runoff SImulation Model) originally developed by Ludwig & Bremicker (2006). LARSIM-ME covers the catchments of the rivers Rhine, Elbe, Weser/Ems, Odra and Upper Danube. The total catchment size simulated by the model is approximately 800,000 km². The spatial resolution is 5 km x 5 km and the computational time-step is daily. For more details about the model see Meißner et al. (2017).</p> <p>The precipitation and temperature data, used to force the hydrological model in simulation mode up to the initialization of the particular forecast, is taken from the E-OBS dataset, version 18 (Haylock et al. 2008). The downward surface solar radiation is extracted from the ERA-Interim reanalysis (Dee et al. 2011) for the period 1979-2018. For further details on data processing see Meißner et al. (2017).</p> <p>As meteorological seasonal forecasts tend to drift towards the model climate with increasing lead-time, the outputs daily total precipitation and air temperature from S4, interpolated to a 50 km x 50 km grid (multiple of the 5 km x 5 km model grid) and from SEAS5, interpolated to a 25 km x 25 km grid, respectively, were drift-corrected with the meteorological observation dataset used for the baseline simulation. As drift correction method the quantile-quantile method (Piani et al. 2010) was used. We corrected daily values of the different variables on a monthly basis, which means each daily value of the same month is corrected by the same scaling. Separate drift correction factors were estimated for each forecast initialization date (calendar month) and monthly lead time (month 1 to month 7) based on the reforecast datasets. In the final step the corrected precipitation and temperature were downscaled to the 5 km by 5 km model grid and used as forcing to create the streamflow re-forecast dataset with LARSIM-ME.</p> <p><strong>Dataset Q_OBS_DE.nc:</strong></p> <p>Mean daily observed flow of the gauges Kaub, Koeln, Ruhrort / Rhine, Pfelling, Hofkirchen / Danube, Desden, Magdeburg Strombruecke, Neu-Darchau / Elbe for the period 1951–2017 stored as variable <strong><em>q_obs(time=24472, stations=8</em>)</strong>.</p> <p>Data originate from the database of gauge measurements of the Federal Waterways and Shipping Administration (WSV). These data were quality checked and published by the gauge-operating WSV offices. Nevertheless, data errors and inconsistencies cannot be ruled out completely, so that neither the WSV nor the BfG do accept any liability for the correctness and completeness of the data. Data source: "German Federal Waterways and Shipping Administration (WSV)", provided by the German Federal Institute of Hydrology (BfG)</p> <p><a href="https://zenodo.org/record/3696446">https://zenodo.org/record/3696446</a></p> <p><strong>Dataset Q_EOBS_LME.nc:</strong></p> <p>Mean daily simulated flow of the hydrological model LARSIM-ME forced by observed meteorology from the EOBS dataset and ERA-Interim stored as variable <em><strong>q_sim (time=13880, stations=8)</strong></em>. Period 1979-2016, Gauges Kaub, Koeln, Ruhrort / Rhine, Pfelling, Hofkirchen / Danube, Desden, Magdeburg Strombruecke, Neu-Darchau / Elbe.</p> <pre><code>float q_sim(time=13880, stations=8); :units = "m3/s"; :_FillValue = -9999.0f; // float :long_name = "simulated streamflow"; :coordinates = "lat lon";</code></pre> <p><strong>Dataset Q_System4_LME.nc:</strong></p> <p>Mean daily forecasted flow of the hydrological model LARSIM-ME forced by air temperature and precipitation of ECMWF’s Seasonal Forecast System 4 re-forecasts initialized 1st of each month for the years 1981-2016 with a lead time of 7 months. Gauges Kaub, Koeln, Ruhrort / Rhine, Pfelling, Hofkirchen / Danube, Desden, Magdeburg Strombruecke, Neu-Darchau / Elbe.</p> <p>Forecast values are stored as variable <em><strong>q_fcast_ens(time=432, lead_time=215, realization=51, stations=8)</strong></em>, first dimension forecast dates, second dimension lead time, third dimension realization, fourth dimension stations.</p> <pre><code>loat q_fcast_ens(time=432, lead_time=215, realization=51, stations=8); :_FillValue = -9999.0f; // float :long_name = "forecast streamflow ensemble"; :units = "m3/s"; :coordinates = "lat lon";</code></pre> <p><strong>Dataset Q_SEAS5_LME.nc:</strong></p> <p>Mean daily forecasted flow of the hydrological model LARSIM-ME forced by air temperature and precipitation of ECMWF’s Seasonal Forecast System SEAS5 re-forecasts initialized 1st of each month for the years 1981-2016 with a lead time of 7 months. Gauges Kaub, Koeln, Ruhrort / Rhine, Pfelling, Hofkirchen / Danube, Desden, Magdeburg Strombruecke, Neu-Darchau / Elbe.</p> <p>Forecast values are stored in the variable <em><strong>q_fcast_ens(time=432, lead_time=215, realization=25, stations=8)</strong></em>, first dimension forecast dates, second dimension lead time, third dimension ensemble member, fourth dimension stations.</p> <pre><code>float q_fcast_ens(time=432, lead_time=215, realization=25, stations=8); :_FillValue = -9999.0f; // float :long_name = "forecast streamflow ensemble"; :units = "m3/s"; :coordinates = "lat lon";</code></pre> <p><strong>Literature</strong></p> <p>Arnal, L., H. Cloke, L. Magnusson, B. Klein, D. Meissner, A. de Tomas, J. Hunink, I. Pechlivanidis, L. Crochemore, S. Suarez, A. Solera, J. Andreu, J. Knight, F. Liggins, A. Weerts, M. H. Ramos & G. Thirel (2017): The sensitivity of sub-seasonal to seasonal streamflow forecasts to meteorological forcing quality, modelled hydrology and the initial hydrological conditions. Deliverable 4.2, IMPREX - Improving Predictions of Hydrological Extremes - Grant Agreement Number 641811, <a href="http://www.imprex.eu/system/files/generated/files/resource/d4-2-imprex-v1-0.pdf">http://www.imprex.eu/system/files/generated/files/resource/d4-2-imprex-v1-0.pdf</a></p> <p>Dee, D. P., S. M. Uppala, A. J. Simmons, P. Berrisford, P. Poli, S. Kobayashi, U. Andrae, M. A. Balmaseda, G. Balsamo, P. Bauer, P. Bechtold, A. C. M. Beljaars, L. van de Berg, J. Bidlot, N. Bormann, C. Delsol, R. Dragani, M. Fuentes, A. J. Geer, L. Haimberger, S. B. Healy, H. Hersbach, E. V. Holm, L. Isaksen, P. Kallberg, M. Kohler, M. Matricardi, A. P. McNally, B. M. Monge-Sanz, J. J. Morcrette, B. K. Park, C. Peubey, P. de Rosnay, C. Tavolato, J. N. Thepaut & F. Vitart (2011): The ERA-Interim reanalysis: configuration and performance of the data assimilation system. Quarterly Journal of the Royal Meteorological Society 137(656), 553-597</p> <p>ECMWF (2017): SEAS5 user guide - Version 1.1. ECMWF, Reading, UK</p> <p>Haylock, M. R., N. Hofstra, A. M. G. Klein Tank, E. J. Klok, P. D. Jones & M. New (2008): A European daily high-resolution gridded data set of surface temperature and precipitation for 1950–2006. Journal of Geophysical Research: Atmospheres 113(D20), D20119</p> <p>Johnson, S. J., T. N. Stockdale, L. Ferranti, M. A. Balmaseda, F. Molteni, L. Magnusson, S. Tietsche, D. Decremer, A. Weisheimer, G. Balsamo, S. Keeley, K. Mogensen, H. Zuo & B. Monge-Sanz (2018): SEAS5: The new ECMWF seasonal forecast system. Geosci. Model Dev. Discuss. 2018, 1-44</p> <p>Ludwig, K. & M. Bremicker (2006): The Water Balance Model LARSIM –Design, Content and Applications. 22. C. Leibundgut, S. Demuth and J. Lange (Eds), Freiburger Schriften zur Hydrologie, Institut für Hydrologie, Universität Freiburg im Breisgau, Freiburg, 141 pp.</p> <p>Meißner, D., B. Klein & M. Ionita (2017): Development of a monthly to seasonal forecast framework tailored to inland waterway transport in central Europe. Hydrol. Earth Syst. Sci. 21(12), 6401-6423</p> <p>Molteni, F., T. Stockdale, M. Balmaseda, G. Balsamo, R. Buizza, L. Ferranti, L. Magnusson, K. Mogensen, T. Palmer & F. Vitart (2011): The new ECMWF seasonal forecast system (System 4). ECMWF Research Department Technical Memorandum n. 656, Shinfield Park, Reading</p> <p>Owens, R. & T. R. E. Hewson (2018): ECMWF Forecast User Guide. ECMWF, Reading, doi: 10.21957/m1cs7h</p> <p>Piani, C., J. O. Haerter & E. Coppola (2010): Statistical bias correction for daily precipitation in regional climate models over Europe. Theoretical and Applied Climatology 99(1-2), 187-192</p> <p>Weerts, A., F. Silvestro, L. Magnusson, B. Klein, I. Pechlivanidis, F. Wetterhall, D. Lavers, E. Gascon, J. Day, S. Hagelin, M. Lindskog & B. van Osnabrugge (2019): Forecast skill developments. Deliverable 4.3, IMPREX - Improving Predictions of Hydrological Extremes - Grant Agreement Number 641811</p>
Enhancing accuracy of air quality and temperature forecasts during paddy crop-residue burning season in Delhi via chemical data assimilation
<p>This paper examines the accuracy of Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) generated 72 h fine particulate matter (PM<sub>2.5</sub>) forecasts in Delhi during the crop residue burning season of Oct-Nov 2017 with respect to assimilation of the Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol optical depth (AOD) retrievals, persistent fire emission assumption, and aerosol-radiation interactions. The assimilation significantly pushes the model AOD and PM<sub>2.5</sub> towards the observations with the largest changes below 5 km altitude in the fire source regions (northeastern Pakistan, Punjab, and Haryana) as well as the receptor New Delhi. WRF-Chem forecast with MODIS AOD assimilation, aerosol-radiation feedback turned on, and real-time fire emissions reduce the mean bias by 88-195 µg/m<sup>3</sup> (70-86%) with the largest improvement during the peak air pollution episode of 6-13 November 2017. Aerosol-radiation feedback contributes ~21%, ~25%, and ~24% to reduction in mean bias of the first, second, and third day of PM<sub>2.5 </sub>forecast. Persistence fire emission assumption is found to work really well, as the accuracy of PM<sub>2.5</sub> forecasts driven by persistent fire emissions was only 6% lower compared to those driven by real fire emissions. Aerosol-radiation feedback extends the benefits of assimilating satellite AOD beyond PM<sub>2.5</sub> forecasts to surface temperature forecast with a reduction in the mean bias of 0.9<sup>o</sup>C - 1.5<sup>o</sup>C (17-30%). These results demonstrate that air quality forecasting can benefit substantially from satellite AOD observations particularly in developing countries that lack resources to rapidly build dense air quality monitoring networks.</p>
Seasonal footprints on ecological time series and jumps in dynamic states of protein configurations from a nonlinear forecasting method characterization (DataSet)
<p>Data from the article: <br>"Seasonal footprints on ecological time series and jumps in dynamic states of protein configurations from a nonlinear forecasting method characterization", L. Reyes, K. Campos, G. D. Avendaño, L. González-Paz, A. Vivas, Y. J. Alvarado, and S. Flores.</p> <p>Data to be used with some implementation of the forecasting method of reference:<br>Sugihara G. and May R. M., Nonlinear forecasting as a way of distinguishing chaos from measurement error in time series, <em>Nature</em> <strong>344</strong>, 734–741 (1990).</p>
IMPREX - TUC - Local scale seasonal hydrological and drought indices forecasts
<p>The compressed files are seasonal forecasts of discharge based on ECMWF System4 (1981-2003) and GLOSEA5 (1996-2003) for the Koutsoulidis catchment, Crete, Greece. The location of the Koutsoulidis catchment as well as the methodology of data development and validation are described in the open access publication by Grillakis et al., 2018 (<a href="https://www.mdpi.com/2073-4441/10/11/1593">https://www.mdpi.com/2073-4441/10/11/1593</a>). Seasonal forecasts of Standardized Precipitation Index (SPI) based on ECMWF System4 (1981-2009) and GLOSEA5 (1996-2009) for the Messara catchment are also included. A description of the study site and methodology is included in the poster attached in the compressed file (EGU201712072-Koutroulis).</p>
Forecasting model of seasonal dynamics of boll weevil Anthonomus grandis grandis (Coleoptera: Curculionidae) in cotton crops using artificial neural networks
Open the record for dataset details and reuse information.
Dataset - "An Adaptive Multi-Seasonal ARIMA Approach for Domestic Hot Water Load Forecasting: A Pilot Study"
<p>The description of the data is in README.txt.</p>
Seasonal upwelling forecasts in the California Current System
<div> <div> <div> <div> <div> <div> <div> <p>Daily mean upwelling forecasts used for analysis in Amaya et al. <em>in review</em>, "Seasonal upwelling forecasts in the California Current System". Forecasts are based on daily mean wind stress data from four initialized hindcasts: CanESM5, CanCM4i, CESM1, and GFDL-SPEAR. Forecasts are for the period 1991-2021.</p> </div> </div> </div> </div> </div> </div> </div> <div> <div> <div> <div> <div> </div> </div> </div> </div> </div>
R code to accompany 'Teleconnection-based evaluation of seasonal forecast quality''
<p>R code for the diagnostic published in 'Teleconnection-based evaluation of seasonal forecast quality' DOI: 10.1007/s00382-020-05327-x</p>
S1 What Are the Key Drivers Controlling the Qualityof Seasonal Streamflow Forecasts?
<p>Recent technological advances in representation of processes in numerical climate models have led to skillful predictions, which can consequently increase the confidence of hydrological predictions and usability of hydroclimatic services. Given that many water-related stakeholders are affected by seasonal hydrological variations, there is a need to manage such variations to their advantage through better understanding of the drivers that influence hydrological predictability. Here we analyze the seasonal forecasts of streamflow volumes across about 35,400 basins in Europe, which lie along a strong gradient in terms of climatology, scale, and hydrological regime. We then link the seasonal volumetric errors to various physiographic-hydroclimatic descriptors and meteorological biases in order to identify the key drivers controlling predictability. Streamflow volumes over Europe are well predicted, yet with some geographic and seasonal variability; however, the predictability deteriorates with increasing lead time particularly in the winter months. Nevertheless, we show that the forecast quality is well correlated to a set of descriptors, which vary depending on the initialization month. The forecast quality of seasonal streamflow volumes is strongly dependent on the basin's hydrological regime, with limited predictability in relatively flashy basins. On the contrary, snow and/or baseflow dominated regions with long recessions show high streamflow predictability. Finally, climatology and precipitation forecast biases are also related to streamflow predictability, highlighting the importance of developing robust bias adjustment methods. Overall, this investigation shows that the seasonal streamflow predictability can be clustered, and hence regionalized, based on a priori knowledge of local hydroclimatic conditions.</p>
Supplementary data for "Timely vaccine strain selection and genomic surveillance improves evolutionary forecast accuracy of seasonal influenza A/H3N2"
<p>Supplementary materials associated with the manuscript by Huddleston and Bedford titled "Timely vaccine strain selection and genomic surveillance improves evolutionary forecast accuracy of seasonal influenza A/H3N2".</p>
Seasonal forecasts of streamflow for Umealven
<p>Seasonal forecasts for the Umealven catchment in northern Sweden between February 1979 and October 2016. The EHYPE model was forced with hydroGFD-v2 data and initialized on the first of each month with a seven-month lead time.</p> <p>The xobc.nc file contains satellite-derived AET, PET, SWE, FSC and the ini situ measurements of streamflow. The time window covers the period 19790101-20201231 although the individual data sets have differing lengths.</p>
Surface and subsurface summer marine heatwave output from CMCC-SPS3.5 seasonal forecast system, GREP reanalysis and ESA CCI SST.
<p>The attached data files were used to produce the figures and analysis in the following study:</p> <p>McAdam, R., Masina, S., Gualdi. S<em>.</em> Seasonal forecasting of subsurface marine heatwaves. <em>Nature Comms. Earth & Env. </em>(2023). </p>
BSC Post-processed Seasonal Climate Forecast for vineyard management
<p>The Climate Services Team at the Barcelona Supercomputing Center has deployed a climate service for vineyard management in the context of the vitiGEOSS project. This dataset results from post-processing, i.e. by downscaling, calibrating and assessing, the seasonal climate prediction system SEAS5 (ECMWF).</p> <p>Probabilistic predictions have as output several solutions (ensemble members) to account for forecast uncertainty. The forecast information is conveyed as probabilities, in this case as the probabilities of occurrence of three categories or terciles (below normal, normal and above normal). The categories are defined based on the terciles of the model climatology distribution over a period in the past. Additional information regarding the probability of occurrence of extremes is also provided, considered as the probability of not reaching the 10th percentile or surpassing the 90th percentile of the model climatology distribution. The skill scores provide information on the forecast quality (fair Ranked Probability Skill Score for the tercile categories and fair Brier Skill Score for the probabilities of extremes). A positive skill score indicates that the prediction is good (better than using average past conditions) in the long term. In contrast, a negative skill score indicates a prediction is not beating the climatological forecast.</p> <ul> <li> <p>Prediction system: European Center for Medium-Range Weather Forecasts (ECMWF) SEAS5 and post-processed by BSC.</p> </li> <li> <p>Issue frequency: Monthly (~15th of each month)</p> </li> <li> <p>Lead times: months 1 to 3 (e.g. For a forecast initialised in June, forecasts will be monthly averages for July, August and September). The initialization date is indicated in the name of each file (e.g. 20210701). </p> </li> <li> <p>Variables: mean, minimum and maximum 2 m temperature, accumulated precipitation, incoming solar radiation</p> </li> <li> <p>Ensemble size: 51 members</p> </li> <li> <p>Postprocessing: Downscaling from original (1°x 1°) resolution to 0.1°x 0.1° for three domains and monthly calibration with variance inflation. </p> </li> <li> <p>Spatial coverage of the domains: </p> </li> <ul> <li> <p>Catalonia region is indicated by ‘cat’ and covers latitudes [10 N, 44 N], and longitudes [1 W, 4 E]. The latitude indices range [1:41], and the longitude indices range [1:51].</p> </li> <li> <p>Douro region is indicated by ’douro’ and covers latitudes [40 N, 43N ] and longitudes [9 W, 6 W]. The latitude indices range [1:31], and the longitude indices range [1:31].</p> </li> <li> <p>Campana region is indicated by ‘campania’ and covers latitudes [39 N, 43 N] and longitudes [13 E,17.3 E]. The latitude indices range [1:41], and the longitude indices range [1:44]. </p> </li> </ul> </ul> <p> </p> <p>For each prediction, there are several files containing the seasonal variables values, probabilities, definition of the categories and skill scores.</p> <ul> <li> <p>Forecast probabilities</p> </li> </ul> <p>E.g t2_campania_prob_20210701.ncml</p> <p>The file name contains the name of the variable, domain, the label ‘prob’ and the initialization date of the forecasts (1st of the month).</p> <p>It contains the forecast probabilities in (%) of each tercile category below normal (prob_bn), normal (prob_n) and above normal (prob_an) and the probability of lower extreme (prob_bp10) and the probability of upper extreme (prob_ap90). The latitude, longitude, and lead time (months 1 to 3) can be selected.</p> <p><strong> </strong></p> <ul> <li> <p>Forecast ensemble members</p> </li> </ul> <p> E.g. t2_campania_20210701.ncml</p> <p>The file name contains the name of the variable, domain and initialization date of the forecasts (1st of the month).</p> <p>It contains the 51 absolute values of the forecast variables in their corresponding units (see Table 2). The latitude, longitude, and lead time (months 1 to 3) can be selected.</p> <p><strong> </strong></p> <ul> <li> <p>Category limits</p> </li> </ul> <p>E.g. t2_campania-percentiles_month07.ncml</p> <p>The file name contains the name of the variable, domain, the label ‘percentiles’ and the month for which the category limits apply. </p> <p>It contains the limits of the predicted categories ( below normal, normal and above normal). These categories are defined with respect to a period in the past. The 33rd, 66th percentiles (p33 and p66) divide the model climatological distribution into 3 equiprobable categories. The 33th percentile is the boundary between below normal and normal, and the 66th percentile is the boundary between the normal and above normal categories. The 10th and 90th percentiles, which define the threshold for the lower and upper extreme conditions, are also provided (p10 and p90). It should be noted that the definition of the categories is specific to each location (latitude and longitude), initialization month and lead time (valid month).</p> <p><strong> </strong></p> <ul> <li> <p>Skill scores</p> </li> </ul> <p>E.g t2_campania-skill_month07.ncml</p> <p>The file name contains the name of the variable, domain, the label ‘skill’ and the month for which the skill scores apply. </p> <p>It contains the measures of forecast quality, the fair Ranked probability score for terciles (rpss) and the fair Brier Skill Score for lower and upper extremes (bsp10 and bsp90). It should be noted that the skill level is specific to each location (latitude and longitude), initialization month and lead time (valid month).</p>
Opportunities for seasonal forecasting to support water management outside the tropics: Supplementary information
<p>Supporting information for Jackson-Blake et al. (2022), 'Opportunities for seasonal forecasting to support water management outside the tropics', HESS</p> <p>Contents (section numbers refer to sections in the HESS paper):</p> <ul> <li>SI1_CatchmentMaps.pdf: Catchment maps for each of the five case study sites.</li> <li>SI2_FirstSurvey_HistoricEvent.pdf: Questions asked as part of the first assessment exercise described in Section 3. In this exercise, stakeholders were asked to pick a historic season when there were problems in their catchment/lake, and then seasonal forecasts were produced for that season. Stakeholders were then asked a set of questions designed to assess: (1) their interpretation of the forecasts, and (2) to what extent they thought forecasts would have been useful for them. Full questions and answers are provided in this pdf.</li> <li>SI3_WindowsOfOpportunity_Responses.xlsx: Full responses to the questionnaire described in Sections 2.2 and 3.2.</li> </ul>
BSC Post-processed Sub-seasonal Climate Forecast for vineyard management
<p>The Climate Services Team at the Barcelona Supercomputing Center has deployed a climate service for vineyard management in the context of the vitiGEOSS project. This dataset results from post-processing, i.e. by downscaling, calibrating and assessing, the subeasonal climate prediction system NCEP-CFSv2.</p> <p>Probabilistic predictions have as output several solutions (ensemble members) to account for forecast uncertainty. The forecast information is conveyed as probabilities, in this case as the probabilities of occurrence of three categories or terciles (below normal, normal and above normal). The categories are defined based on the terciles of the model climatology distribution over a period in the past. Additional information regarding the probability of occurrence of extremes is also provided, considered as the probability of not reaching the 10th percentile or surpassing the 90th percentile of the model climatology distribution. The skill scores provide information on the forecast quality (fair Ranked Probability Skill Score for the tercile categories and fair Brier Skill Score for the probabilities of extremes). A positive skill score indicates that the prediction is good (better than using average past conditions) in the long term, while a negative skill score indicates a prediction is not beating the climatological forecast.</p> <ul> <li> <p>Prediction system: National Centers for Environmental Prediction (NCEP) CFSv2, post-processed by BSC (create a lagged ensemble, downscaling and calibration).</p> </li> <li> <p>Issue frequency: Weekly (Initialization every Thursday, post-processed prediction every Friday).</p> </li> <li> <p>Lead times: weeks 1 to 4 (e.g. For a forecast issued on Friday 4th November, forecasts will be weekly averages starting the following Monday-Thursday and the 4 following weeks (e.g. week 1 will be 8th-15th November). The initialization date is indicated in the name of each file (e.g. 20211104). </p> </li> <li> <p>Variables: mean, minimum and maximum 2 m temperature, accumulated precipitation, and incoming solar radiation.</p> </li> <li> <p>Ensemble size: 48 members</p> </li> <li> <p>Postprocessing: Create a lagged ensemble of 48 ensemble members, downscaling from the original (1°x 1°) resolution to 0.1°x 0.1° for the three domains and weekly calibration with variance inflation. </p> </li> <li> <p>Spatial coverage of the domains: </p> </li> <ul> <li> <p>Catalonia region is indicated by ‘cat’ and covers latitudes [10 N, 44 N], and longitudes [1 W, 4 E]. The latitude indices range [1:41], and the longitude indices range [1:51].</p> </li> <li> <p>Douro region is indicated by ‘douro’ and covers latitudes [40 N, 43N ] and longitudes [9 W, 6 W]. The latitude indices range [1:31], and the longitude indices range [1:31].</p> </li> <li> <p>Campana region is indicated by ‘campania’ and covers latitudes [39 N, 43 N] and longitudes [13 E,17.3 E]. The latitude indices range [1:41], and the longitude indices range [1:44]. </p> </li> </ul> </ul> <p>The specific latitude and longitude indices to extract the predictions corresponding to each vitiGEOSS site are indicated in Table 2.</p> <ul> <li> <p>Forecast probabilities</p> </li> </ul> <p>E.g t2_campania_prob_20211104.ncml</p> <p>The file name contains the name of the variable, domain, the label ‘prob’ and the initialization date of the forecasts (Always a Thursday).</p> <p>It contains the forecast probabilities in (%) of each tercile category below normal (prob_bn), normal (prob_n) and above normal (prob_an) and the probability of lower extreme (prob_bp10) and the probability of upper extreme (prob_ap90). The latitude, longitude and lead time (weeks 1 to 4) can be selected.</p> <ul> <li> <p>Forecast ensemble members</p> </li> </ul> <p> E.g. t2_campania_20211104.ncml</p> <p>The file name contains the name of the variable, domain and initialization date of the forecasts (Always a Thursday).</p> <p>It contains the 48 absolute values of the forecast variables in their corresponding units (see Table 2). The latitude, longitude and lead time (weeks 1 to 4) can be selected.</p> <ul> <li> <p>Category limits</p> </li> </ul> <p>E.g. t2_campania_percentiles_week44.ncml</p> <p>The file name contains the name of the variable, domain, the label ‘percentiles’ and the month for which the category limits apply. </p> <p>It contains the limits of the predicted categories ( below normal, normal and above normal). These categories are defined with respect to a period in the past. The 33rd, 66th percentiles (p33 and p66) divide the model climatological distribution into 3 equiprobable categories. The 33rd percentile is the boundary between below-normal and normal, and the 66th percentile is the boundary between the normal and above-normal categories. The 10th and 90th percentiles, which define the threshold for the lower and upper extreme conditions, are also provided (p10 and p90). It should be noted that the definition of the categories is specific to each location (latitude and longitude), initialization month and lead time (valid month).</p> <ul> <li> <p>Skill scores</p> </li> </ul> <p>E.g t2_campania_skill_week44.ncml</p> <p>The file name contains the name of the variable, domain, the label ‘skill’ and the week of the year for which the skill scores apply. </p> <p>It contains the measures of forecast quality, the fair Ranked probability score for terciles (rpss) and the fair Brier Skill Score for lower and upper extremes (bsp10 and bsp90). It should be noted that the skill level is specific to each location (latitude and longitude), initialization and lead time (valid week).</p>
On the dominant factor controlling seasonal hydrological forecast skill in China
<p>This is the open-access material for Paper water-228727, which is used to identify the dominant factor controlling seasonal hydrological forecast skill over China. This material contains three files, namely DATA.rar, CODE.rar, and README.txt. The first two files are archived folders with the dataset, codes and scripts for each figure of this paper inclusive. The README.txt gives a detailed introduction of the file structures within each archive (i.e., DATA and CODE). </p> <p> </p>
Seasonal ocean forecasts to improve predictions of Dungeness crab catch rates, co-developed with state and tribal fishery managers
<p>Historical (hindcast) and September initialized forecast fields from the J-SCOPE system (more information here: https://www.nanoos.org/products/j-scope/). The historical fields are annual averages while the forecasts are averaged monthly. The fields here were used to generate the CPUE models described in Norton et al. (2023). </p> <p>Abstract from the paper: The commercial Dungeness crab (Metacarcinus magister) fishery in Oregon and Washington (USA) is one of the most valuable fisheries in the region, but it experiences high interannual variability. These fluctuations have been attributed to environmental drivers on seasonal and annual timescales. In this study, researchers and state and tribal fisheries managers develop a statistical model for Dungeness crab catch per unit effort (CPUE) to help inform dynamic management decisions in Oregon and Washington. Fishing observations were matched to seasonally forecast and lagged ocean conditions from J-SCOPE, a regional forecast system (http://www.nanoos.org/products/j-scope/). Inclusion of dynamic and lagged ocean conditions improved model skill compared to simpler models, and the best model captured intraseasonal trends and interannual variability in catch rates, and spatial catch patterns. We also found that model skill relied on fishing behaviour, which varies interannually, highlighting the need for advanced fishing behaviour modelling to reduce uncertainty. The relationships between catch rates and ocean conditions may help elucidate environmental influences of catch variability. Forecast products were co-designed with managers to meet their needs for key decision points. Our results illustrate a seasonal forecasting approach for management of other highly productive, but also dynamic, invertebrates that increasingly contribute to global fisheries yield.</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.