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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>
ClepsHresEns-HbvRhein134-SbkReRhein Medium Range Waterlevel Ensemble Forecasts for Waterway Rhine
<p>The datasets provided here were produced as part of the IMPREX project for work package 9, task 3 “<em>Case studies</em>”. Analysis of the datasets are published in Deliverable 9.2 “<em>Framework for the assessment of forecast quality and value in the navigation sector</em>“(Klein & Meißner 2017) and Deliverable 9.4 “<em>Semi-operational forecasting system for Rhine, Danube and Elbe to support improved transport cost planning</em>“ (Klein & Meißner 2019). The aim of the dataset was to apply the statistical post-processing method Ensemble Model Output Statistics EMOS (Gneiting et al. 2005) to estimate the predictive uncertainty of the waterlevel ensemble forecasts, in order to provide probabilistic water level forecasts to the end users (Klein & Meißner 2019).</p> <p>Meteorological forcing data used to calculate water-level forecasts with an extended forecast horizon are based on a 68 member multi-model ensemble: 51 ensemble members from ECMWF ENS (1 control forecast and 50 perturbed members) as well as the control forecast ECMWF HRES with a higher spatial resolution (Leutbecher & Palmer 2008, Owens & Hewson 2018), and the 16 members of the limited-area ensemble prediction system by the consortium for small-scale modelling COSMO LEPS (Montani et al. 2011, Marsigli et al. 2014). Archived meteorological real-time forecasts of the period January 2008 to December 2015 have been used to produce this comprehensive water level re-forecast data set.</p> <p>The conceptual, semi-distributed rainfall-runoff model HBV-96 (Bergström 1995, Lindstrom et al. 1997) is applied to calculate the flow forecasts used as boundary conditions and lateral inflows of the hydrodynamic model SOBEK (Deltares 2012) used to calculate water level forecasts along the river Rhine. The river Rhine basin is divided into 134 subbasins which are further subdivided into hydrological response units (HRU) according to land use and elevation classes. The flow formation processes are calculated on those HRUs. The model calculates flow with a temporal resolution of 1 h using temperature and precipitation fields that have been interpolated over the subbasins as meteorological input.</p> <p>The hydrodynamic model suite SOBEK is used as one-dimensional model, which uses cross-section information of the River Rhine as well as its main tributaries. The distance between the cross-sections, which cover the river bathymetry as well as its floodplains, is non-equidistant and ranges between 100 m and 800 m. As the main tributaries of the River Rhine are impounded rivers (e.g. Moselle, Main) the SOBEK-model includes several weirs with their specific control rules in order to simulate the real behaviour of these elements, too.</p> <p>The flow and water level forecasts were initialized each day at 06:00 UTC, which means that observed real-time meteorological data, interpolated to the subbasins of the hydrological model, up to the forecast date were used as forcings of the hydrological model and observed flow was used as input for the hydrodynamic model to initialize the model states. For the forecast period meteorological ensemble runs from the different Numerical Weather Prediction (NWP) models interpolated to the subbasins were used as forcings of the hydrological model. Flow forecasts of the large tributaries of the river Rhine simulated with HBV were then used as input for the hydrodynamic model. To reduce the error of the input to the hydrodynamic model autoregressive error correction models (Broersen & Weerts 2005) was applied using the differences between the simulation of the model using meteorological observations as forcings and the actually past flow observations as training data. This error correction reduces the error of the hydrological model at the forecast initialization time to zero. To reduce the error of the waterlevel forecasts obtained by running the hydrodynamic model, again autoregressive error correction models were applied using the differences between the water level simulation using observed flow as input and the water-level observations of the past.</p> <p><strong>Dataset H_OBS_RHINE.nc:</strong></p> <p>Hourly observed water levels of the gauges Kaub, Koeln, Ruhrort / Rhine for the period 2008–2016 stored as variable <em><strong>h_obs(time=78912, stations=3).</strong></em></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> <pre><code>float h_obs(time=78912, stations=3); :units = "cm"; :_FillValue = -9999.0f; // float :long_name = "observed waterlevel"; :coordinates = "lat lon";</code></pre> <p><strong>Dataset H_MM_HBV134_SOBEK.nc</strong></p> <p>Hourly forecasted water level of the hydrodynamic mode SOBEK forced by flow forecasts of the hydrological model HBV134 forced by a multi-model meteorological ensemble. Daily forecasts initialized at 06:00 UTC of the period 2008-01-01 to 2015-12-31 with a lead time of 240 hours. Gauges Kaub, Koeln, Ruhrort / Rhine.</p> <p>Forecast values are stored in the variable <em><strong>h_fcast_ens(time=2869, lead_time=241, realization=68, stations=3)</strong></em>, first dimension forecast dates, second dimension lead time, third dimension realization, fourth dimension station. ECMWF-HRES first realization, COSMO-LEPS realization 2 – 17, ECMWF-ENS realization 18- 68.</p> <pre><code>float h_fcast_ens(time=2869, lead_time=241, realization=68, stations=3); :_FillValue = -9999.0f; // float :long_name = "forecast waterlevel ensemble"; :units = "cm"; :coordinates = "lat lon";</code></pre> <p><strong>Literature</strong></p> <p>Bergström, S. (1995): The HBV model. In: V. P. Singh (Ed.): Computer models of watershed hydrology. Water Resources Publications, Colorado, USA, 443-476</p> <p>Broersen, P. & A. Weerts (2005): Automatic Error Correction of Rainfall-Runoff models in Flood Forecasting Systems. Conference Proceedings: IMTC 2005 – Instrumentation and Measurement Technology Conference, Ottawa, Canada, 17-19 May 2005.</p> <p>Deltares (2012): Technical Reference SOBEK-RE. Deltares, Delft, The Netherlands</p> <p>Gneiting, T., A. E. Raftery, A. H. Westveld & T. Goldman (2005): Calibrated probabilistic forecasting using ensemble model output statistics and minimum CRPS estimation. Monthly Weather Review 133(5), 1098-1118</p> <p>Klein, B. & D. Meissner (2017): Framework for the assessment of forecast quality and value in the navigation sector. Deliverable 9.2, IMPREX - Improving Predictions of Hydrological Extremes - Grant Agreement Number 641811, <a href="http://www.imprex.eu/system/files/generated/files/resource/d9-2-imprex-v2-0.pdf">http://www.imprex.eu/system/files/generated/files/resource/d9-2-imprex-v2-0.pdf</a></p> <p>Klein, B. & D. Meissner (2019): Semi-operational forecasting system for Rhine, Danube and Elbe to support improved transport cost planning. Deliverable 9.4, IMPREX - Improving Predictions of Hydrological Extremes - Grant Agreement Number 641811, <a href="https://imprex.eu/system/files/generated/files/resource/deliverable9-4-imprex-v1-0.pdf">https://imprex.eu/system/files/generated/files/resource/deliverable9-4-imprex-v1-0.pdf</a></p> <p>Leutbecher, M. & T. N. Palmer (2008): Ensemble forecasting. Journal of Computational Physics 227(7), 3515-3539</p> <p>Lindstrom, G., B. Johansson, M. Persson, M. Gardelin & S. Bergstrom (1997): Development and test of the distributed HBV-96 hydrological model. Journal of Hydrology 201(1-4), 272-288</p> <p>Marsigli, C., A. Montani & T. Paccagnella (2014): Perturbation of initial and boundary conditions for a limited-area ensemble: multi-model versus single-model approach. Quarterly Journal of the Royal Meteorological Society 140(678), 197-208</p> <p>Montani, A., D. Cesari, C. Marsigli & T. Paccagnella (2011): Seven years of activity in the field of mesoscale ensemble forecasting by the COSMO-LEPS system: main achievements and open challenges. Tellus Series a-Dynamic Meteorology and Oceanography 63(3), 605-624</p> <p>Owens, R. & T. R. E. Hewson (2018): ECMWF Forecast User Guide. ECMWF, Reading, doi: 10.21957/m1cs7h</p>
ENS-LARSIM_ME Monthly Streamflow Forecasts for German Waterways
<p>The datasets provided here 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>“ and work package 9, task 3 “<em>Case studies</em>”. Analysis of the datasets are published in Deliverable 9.4 “<em>Semi-operational forecasting system for Rhine, Danube and Elbe to support improved transport cost planning</em>“ (Klein & Meißner 2019). The aim was to evaluate the potential skill of monthly streamflow forecasting for the German waterways Rhine, Elbe and Danube.</p> <p>As meteorological forcing data to calculate monthly flow forecasts the extended-range forecasts from ECMWF-ENS are applied. Twice a week (Monday and Thursday), the ENS model is extended to a lead time up to 46 days by ECMWF. The horizontal resolution for the first 15 days is 0.2°x0.2° (approx. 18 km) and from day 15 to day 46 it is 0.4°x0.4° (approx. 36 km). The ensemble consists of 1 control forecasts and 50 perturbed members, made from slightly different initial atmospheric and oceanic conditions (Owens & Hewson 2018). Re-Forecasts are generated with the same model as used for the operational forecasts for the past 20 years, starting on the same day and month as each real time forecast. The ensemble size is 11-member, which means that in total 20 years x 11 members = 220 forecasts are available for each real time forecast date. The re-forecasts are also created twice a week (Mondays and Thursdays) and are available a week in advance.</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>Real-time meteorological station data (precipitation, temperature and global radiation) was interpolated to the 5 km x 5 km model grid and used as meteorological forcing to initialize LARSIM-ME at the forecast date.</p> <p>Re-forecasts for the hindcast dates 10th March 2016 – 09th March 2017 generating re-forecasts of the last 20 years were used. As station density of real-time meteorological station data is limited before 2000, only reforecasts with a forecast date after 1999 were considered. Daily total precipitation, daily mean air temperature and global radiation of the reforecast dataset of ECMWF-ENS were interpolated to the 5kmx5km model grid and used as forcing to create the streamflow re-forecast dataset with LARSIM-ME.</p> <p><strong>Dataset Q_OBS.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>q_obs(time=24472, stations=8)</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_SYNOP_LME.nc:</strong></p> <p>Mean daily simulated flow of the hydrological model LARSIM-ME forced by observed meteorology from real-time meteorological station data stored as variable float <em><strong>q_sim(time=6210, stations=8)</strong></em>. Period 2000-2016, Gauges Kaub, Koeln, Ruhrort / Rhine, Pfelling, Hofkirchen / Danube, Desden, Magdeburg Strombruecke, Neu-Darchau / Elbe.</p> <pre><code>float q_sim(time=6210, stations=8); :units = "m3/s"; :_FillValue = -9999.0f; // float :long_name = "simulated streamflow"; :coordinates = "lat lon";</code></pre> <p><strong>Dataset Q_ENS_LME.nc:</strong></p> <p>Mean daily forecasted flow of the hydrological model LARSIM-ME forced by air temperature, precipitation and global radiation of the ECMWF ENS re-forecasts for the hindcast dates 10th March 2016 – 09th March 2017 with a lead time of 46 days. Forecast dates 2nd January 2000 to 9th March 2016, in total 1699 forecasts. 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=1699, lead_time=46, realization=11, stations=8)</strong></em>, first dimension forecast dates, second dimension lead time, third dimension realization, fourth dimension station.</p> <pre><code>float q_fcast_ens(time=1699, lead_time=46, realization=11, 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>Klein, B. & D. Meissner (2019): Semi-operational forecasting system for Rhine, Danube and Elbe to support improved transport cost planning. Deliverable 9.4, IMPREX - Improving Predictions of Hydrological Extremes - Grant Agreement Number 641811, <a href="https://imprex.eu/system/files/generated/files/resource/deliverable9-4-imprex-v1-0.pdf">https://imprex.eu/system/files/generated/files/resource/deliverable9-4-imprex-v1-0.pdf</a></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</p> <p>Owens, R. & T. R. E. Hewson (2018): ECMWF Forecast User Guide. ECMWF, Reading, doi: 10.21957/m1cs7h</p>
A Machine-Learning-Based Global Atmospheric Forecast Model
<p>Data used in "A Machine-Learning-Based Global Atmospheric Forecast Model" 2020. Included in this dataset is the machine learning predictions and the truth for the year's worth of simulated forecast. </p>
Global ECMWF Fire Forecasting system - sample data for wildfires in Attica (Greece) on 23-26 July 2018
<p>The European Centre for Medium-Range Weather Forecasts (<a href="https://www.ecmwf.int/">ECMWF</a>) produces daily fire danger forecasts and reanalysis products from the Global ECMWF Fire Forecast (<a href="https://git.ecmwf.int//projects/CEMSF/repos/geff/browse">GEFF</a>) model. Reanalysis is available through the Copernicus Climate Data Store (<a href="https://cds.climate.copernicus.eu/cdsapp#!/dataset/cems-fire-historical">CDS</a>) while the medium-range real-time forecast is available through the <a href="https://effis.jrc.ec.europa.eu/static/effis_current_situation/public/index.html">EFFIS</a> and <a href="https://gwis.jrc.ec.europa.eu/static/gwis_current_situation/public/index.html">GWIS</a> platforms.</p> <p>This repository provides sample datasets for the assessment of the fire danger during the Attica (Greece) wildfires occurred on 23-26 July 2018:</p> <ul> <li> <p>ECMWF_EFFIS_20180723_1200_en.tar<br> (ensemble forecasts issued on 2018-07-23, global coverage, all indices)</p> </li> <li> <p>ECMWF_EFFIS_20180723_1200_hr.tar<br> (deterministic forecasts issued on 2018-07-23, global coverage, all indices)</p> </li> <li> <p>ECMWF_EFFIS_20180723-26_1200_hr_e5.tar<br> (deterministic reanalysis based on ERA5 issued for 2018-07-23, global coverage, all indices)</p> </li> <li> <p>ECMWF_EFFIS_20180723-26_1200_en_e5.tar<br> (probabilistic reanalysis based on ERA5 issued for 2018-07-23, global coverage, all indices)</p> </li> <li> <p>ECMWF_EFFIS_20180723-26_e5.tar<br> (probabilistic and deterministic reanalysis based on ERA5 issued for 2018-07-23/26, global coverage, FWI only)</p> </li> <li> <p>bbox.tar, containing 1 index (FWI) for the bounding box:</p> <ul> <li> <p>GEFF-reanalysis, which provides historical records of fire danger conditions in the period 23-26 July 2018</p> <ul> <li> <p>e5_hr, this folder contains deterministic model outputs</p> </li> <li> <p>e5_en, this folder contains probabilistic model outputs (made of 10 ensemble members)</p> </li> </ul> </li> <li> <p>GEFF-realtime provides real-time forecasts (in the period 14-26 July 2018) generated using weather forcings from the latest model cycle of the ECMWF’s Integrated Forecasting System (IFS).</p> <ul> <li> <p>rt_hr, this folder contains high-resolution deterministic forecasts (~9 Km)</p> </li> <li> <p>rt_en, this folder contains probabilistic forecasts (~18Km)</p> </li> </ul> </li> </ul> </li> <li> <p>lon_min = 23, lon_max = 25, lat_min = 37, lat_max = 39</p> </li> </ul> <p><strong>Please note, the sample data provided in this repository is intended to be used for education purposes only (e.g. training courses).</strong></p> <p>These products have been developed as part of the EU-funded Copernicus Emergency Management Services (<a href="https://emergency.copernicus.eu/">CEMS</a>) and complement other Copernicus products related to fire, such as the biomass-burning emissions made available by the Copernicus Atmosphere Monitoring Service (<a href="https://atmosphere.copernicus.eu/">CAMS</a>). The development of the GEFF modelling system was funded through a third-party agreement with the European Commission’s Joint Research Centre (<a href="https://ec.europa.eu/info/departments/joint-research-centre_en">JRC</a>). </p> <p>GEFF produces fire danger indices based on the Canadian Fire Weather index as well as the US and Australian fire danger models. GEFF datasets are under the Copernicus license, which provides users with free, full and open access to environmental data.</p> <p>For more information, please refer to the documentation on the <a href="http://datastore.copernicus-climate.eu/c3s/published-forms/c3sprod/cems-fire-historical/Fire_In_CDS.pdf">CDS</a> and on the <a href="https://effis.jrc.ec.europa.eu/about-effis/technical-background/fire-danger-forecast/">EFFIS website</a>.</p>
Global ECMWF Fire Forecasting system - sample data for wildfires in Sweden on 15-20 July 2018
<p>The European Centre for Medium-Range Weather Forecasts (<a href="https://www.ecmwf.int/">ECMWF</a>) produces daily fire danger forecasts and reanalysis products from the Global ECMWF Fire Forecast (<a href="https://git.ecmwf.int//projects/CEMSF/repos/geff/browse">GEFF</a>) model. Reanalysis is available through the Copernicus Climate Data Store (<a href="https://cds.climate.copernicus.eu/cdsapp#%21/dataset/cems-fire-historical">CDS</a>) while the medium-range real-time forecast is available through the <a href="https://effis.jrc.ec.europa.eu/static/effis_current_situation/public/index.html">EFFIS</a> and <a href="https://gwis.jrc.ec.europa.eu/static/gwis_current_situation/public/index.html">GWIS</a> platforms.</p> <p>This repository provides FWI sample datasets for the assessment of the wildfires occurred in Sweden on 15-20 July 2018:</p> <ul> <li> <p>GEFF-reanalysis, which provides historical records of fire danger conditions</p> <ul> <li> <p>e5_hr, this folder contains deterministic model outputs</p> </li> <li> <p>e5_en, this folder contains probabilistic model outputs (made of 10 ensemble members)</p> </li> </ul> </li> <li> <p>GEFF-realtime provides real-time forecasts generated using weather forcings from the model cycle 45r1 of the ECMWF’s Integrated Forecasting System (IFS).</p> <ul> <li> <p>rt_hr, this folder contains high-resolution deterministic forecasts (~9 Km)</p> </li> <li> <p>rt_en, this folder contains probabilistic forecasts (~18Km)</p> </li> </ul> </li> <li> <p>Geographical bounding box: lon_min = 10.1, lon_max = 24.8, lat_min = 55, lat_max = 69</p> </li> </ul> <p><strong>Please note, the sample data provided in this repository is intended to be used for education purposes only (e.g. training courses).</strong></p> <p>These products have been developed as part of the EU-funded Copernicus Emergency Management Services (<a href="https://emergency.copernicus.eu/">CEMS</a>) and complement other Copernicus products related to fire, such as the biomass-burning emissions made available by the Copernicus Atmosphere Monitoring Service (<a href="https://atmosphere.copernicus.eu/">CAMS</a>). The development of the GEFF modelling system was funded through a third-party agreement with the European Commission’s Joint Research Centre (<a href="https://ec.europa.eu/info/departments/joint-research-centre_en">JRC</a>). </p> <p>GEFF produces fire danger indices based on the Canadian Fire Weather index as well as the US and Australian fire danger models. GEFF datasets are under the Copernicus license, which provides users with free, full and open access to environmental data.</p> <p>For more information, please refer to the documentation on the <a href="http://datastore.copernicus-climate.eu/c3s/published-forms/c3sprod/cems-fire-historical/Fire_In_CDS.pdf">CDS</a> and on the <a href="https://effis.jrc.ec.europa.eu/about-effis/technical-background/fire-danger-forecast/">EFFIS website</a>.</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>
Data from: The size, symmetry, and color saturation of a male guppy's ornaments forecast his resistance to parasites
Sexually selected ornaments range from highly dynamic traits to those that are fixed during development and relatively static throughout sexual maturity. Ornaments along this continuum differ in the information they provide about the qualities of potential mates, such as their parasite resistance. Dynamic ornaments enable real-time assessment of the bearer's condition: they can reflect an individual's current infection status, or resistance to recent infections. Static ornaments, however, are not affected by recent infection but may instead indicate an individual's genetically-determined resistance, even in the absence of infection. Given the typically aggregated distribution of parasites among hosts, infection is unlikely to affect the ornaments of the vast majority of individuals in a population: static ornaments may therefore be the more reliable indicators of parasite resistance. To test this hypothesis, we quantified the ornaments of male guppies, Poecilia reticulata, before experimentally infecting them with Gyrodactylus turnbulli. Males with more left-right symmetrical black coloration and those with larger areas of orange coloration, both static ornaments, were more resistant. However, males with more saturated orange coloration, a dynamic ornament, were less resistant. Female guppies often prefer symmetrical males with larger orange ornaments, suggesting parasite-mediated natural and sexual selection act in concert on these traits.
Data from: Are replication rates the same across academic fields? community forecasts from the DARPA SCORE program
The DARPA program "Systematizing Confidence in Open Research and Evidence" (SCORE) aims to generate confidence scores for a large number of research claims from empirical studies in the social and behavioral sciences. The confidence scores will provide a quantitative assessment of how likely a claim will hold up in an independent replication. To create the scores we follow earlier approaches and use prediction markets and surveys to forecast replication outcomes. Based on an initial set of forecasts for the overall replication rate in SCORE and its dependence on the academic discipline and the time of publication, we show that participants expect replication rates to increase over time. Moreover, they expect replication rates to differ between fields, with the highest replication rate in economics (average survey response 58%), and the lowest in Psychology and in Education (average survey response of 42% for both fields). These results reveal insights into the academic community's views of the replication crisis, including for research fields for which no large-scale replication studies have been undertaken yet.
Plastic Packaging Market Estimates and Forecast, by Type, 2014 - 2025
<p>Estimates of historical (2014-2018) and forecast (2019-2025) size of the global market for rigid and flexible plastic packaging.</p>
Conjunto de dados Modelo de Regressão Aplicado à Previsão de Preços SPOT de Energia Elétrica (Dataset Regression Model Applied to Electric Energy SPOT Price Forecasting)
<p>Esse conjunto de dados utilizou DataSets de duas fontes distintas: CCEE e ONS. Como são órgãos públicos os dados são acurados, transparentes, confiáveis e de boa qualidade. Os dados de entrada possuem as variáveis que são utilizadas no modelo atual do PLD, já citado. São elas: as datas, o armazenamento de água, a ENA, a expectativa de ENA para a próxima semana e a carga.</p> <p>As datas são dados diários entre janeiro de 2013 e janeiro de 2017. O armazenamento de água é dado por submercado e apresentado em porcentagem da capacidade máxima. A ENA e a expectativa dela para a semana seguinte são apresentadas em porcentagem a partir das chuvas realizadas convertidas em MWmédio pelas previsões feitas utilizando dados históricos (1932-2007). A carga está em MWmédio. E o PLD em R$/MWh.</p> <p>Os soma dos dados dos quatro submercados (SE/CO, SU, NE, NO) de cada dado nos fornece a informação do Sistema Nacional Interligado (SIN).</p> <p>As variáveis de carga, armazenamento e ENA foram retiradas do histórico de operações do site da ONS, disponibilizados para download em ‘csv’. E o PLD do site da CCEE, disponibilizados em ‘xls’.</p> <p>Foram mesclados a partir das datas formando o arquivo de entrada para o modelo utilizado nos experimento</p> <p> </p> <p>Metadados / Metadata</p> <p>Storage of water: percentage of storage of water by submarket.<br> ENA and expectative of ENA: presented by percentage of previsions of rains that happen converted on MWmedium by previsions did with a historic (1932-2007). Data are by submarket.<br> Charge: charge by submarket give on MWmedium.<br> PLD: give on R$/MWh<br> The sum of each variable is the data of the total system.</p> <p> </p> <p> </p>
Impact of Different Nesting Methods on the Simulation of a Severe Convective Event Over South Korea Using the Weather Research and Forecasting Model
<p>The data from various platforms (NCEP FNL, TRMM, ERA5, AWS) and WRF Model output utilised to generate the figures in the current study (https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2020JD033084) are available at this Zenodo data repository.</p>
The growth of COVID-19 scientific literature: A forecast analysis of different daily time series in specific settings
<p>Submitted to The ISSI 2021 Conference. The conference is organised by KU Leuven in close collaboration with the university of Antwerp under the auspices of ISSI – the International Society for Informetrics and Scientometrics (<a href="http://www.issi-society.org/">http://www.issi-society.org/</a>). </p> <p>We present a forecasting analysis on the growth of scientific literature related to COVID-19 expected for 2021. Considering the paramount scientific and financial efforts made by the research community to find solutions to end the COVID-19 pandemic, an unprecedented volume of scientific outputs is being produced. This questions the capacity of scientists, politicians and citizens to maintain infrastructure, digest content and take scientifically informed decisions. A crucial aspect is to make predictions to prepare for such a large corpus of scientific literature. Here we base our predictions on the ARIMA model and use two different data sources: the Dimensions and World Health Organization COVID-19 databases. These two sources have the particularity of including in the metadata information on the date in which papers were indexed. We present global predictions, plus predictions in three specific settings: by type of access (Open Access), by NLM source (PubMed and PMC), and by domain-specific repository (SSRN and MedRxiv). We conclude by discussing our findings.</p>
The Multi-Radar Multi-Sensor (MRMS) and the Stage IV rainfall products, and the aggregate forecast statistics for the three real case studies
<p>This is a data repository in support of the article "Impact of Assimilating High-Resolution Atmospheric Motion Vectors on Convective Scale Short-Term Forecasts. Part II: Assimilation Experiments of GOES-16 Satellite Derived Winds" submitted to AGU <em>J. of Advances in Modeling of Earth Systems. </em></p> <p>The data set consists of</p> <ul> <li>The Multi-Radar Multi-Sensor (MRMS) and the Stage IV rainfall products used for validation in the three real case studies.</li> <li>The aggregate forecast statistics for composite reflectivity and APCP for the three real case studies are contained in the zipped files.</li> </ul>
Data from: Spatio-temporally explicit model averaging for forecasting of Alaskan groundfish catch
(1) Fisheries management is dominated by the need to forecast catch and abundance of commercially and ecologically important species. The influence of spatial information and environmental factors on forecasting error is not often considered. We propose a forecasting method called spatio-temporally explicit model averaging (STEMA) to combine spatial and temporal information through model averaging. (2) We examine the performance of STEMA against two popular forecasting models and a modern spatial prediction model: the autoregressive integrated moving averages (ARIMA) model, the Bayesian hierarchical model, and the varying coefficient model. We focus on applying the methods to four species of Alaskan groundfish for which only catch data are available. (3) Our method reduces forecasting errors significantly for most of the tested models when compared to ARIMAX, Bayesian, and varying coefficient methods. We also consider the effect of sea surface temperature (SST) on the forecasting of catch, as multiple studies reveal a potential influence of water temperature on the survival and growth of juvenile groundfish. For most of the preferred models, inclusion of SST in the model improved forecasting of catch. (4) It is advisable to consider both spatial information and relevant environmental factors in forecasting models to obtain more accurate projections of population abundance. The STEMA method is capable of accounting for spatial information in forecasting and can be applied to various types of data because of its flexible varying coefficient model structure. It is therefore a suitable forecasting method for application to many fields including ecology, epidemiology, and climatology.
Data from: Landscape heterogeneity is key to forecasting outcomes of plant reintroduction
Conservation and restoration projects often involve starting new populations by introducing individuals into portions of their native or projected range. Such efforts can help meet many related goals, including habitat creation, ecosystem service provisioning, assisted migration, and the reintroduction of imperiled species following local extirpation. The outcomes of reintroduction efforts, however, are highly variable, with results ranging from local extinction to dramatic population growth; reasons for this variation remain unclear. Here, we ask whether population growth following plant reintroductions is governed by variation at two scales: the scale of individual habitat patches to which individuals are reintroduced, and larger among-landscape scales in which similar patches may be situated in landscapes that differ in matrix type, soil conditions, and other factors. Quantifying demographic variation at these two scales will help prioritize locations for introduction and, once introductions take place, forecast population growth. This work took place within a large-scale habitat fragmentation experiment, where individuals of two perennial forb species were reintroduced into eight replicate ~50 ha landscapes, each containing a set of five ~1 ha patches that varied in their degree of isolation (connected by habitat corridors or unconnected) and edge-to-area ratio. Using data on individual growth, survival, reproductive output, and recruitment collected one to two years after reintroduction, we developed models to forecast population growth, then compared forecasts to observed population sizes, three and six years later. Both the type of patch (connected and unconnected) and identity of the landscape to which individuals were reintroduced had effects on forecasted population growth rates, but only variation associated with landscape identity was an accurate predictor of subsequently observed population growth rates. Models that did not include landscape identity had minimal forecasting ability, revealing the key importance of variation at this scale for accurate prediction. Of the five demographic rates used to model population dynamics, seed production was the most important source of forecast error in population growth rates. Our results point to the importance of accounting for landscape-scale variation in demographic models and demonstrate how such models might assist with prioritizing particular landscapes for species reintroduction projects.
Supplementary video content for "Three-dimensional visualization of ensemble weather forecasts", Parts 1 and 2 (Geoscientific Model Development, 2015)
<p>Supplementary video content (full resolution) for the papers "Three-dimensional visualization of ensemble weather forecasts - Part 1: The visualization tool Met.3D (version 1.0)" and "Three-dimensional visualization of ensemble weather forecasts - Part 2: Forecasting warm conveyor belt situations for aircraft-based field campaigns", Geoscientific Model Development, 2015. The corresponding papers can be found on http://geosci-model-dev.net/.</p>
Data for assessment of rainfall forecasts over eastern China with the GRIST
<p>This datasets provides the data and codes for JGR-A(2024JD042811).</p>
WRF-Solar AOD550 & Clear-Sky Irradiance Forecasts & Verifying Observations over CONUS
<p>This dataset is fully described in and is a companion of Lee et al. (2022):</p> <p>Lee, J. A., P. A. Jiménez, J. Dudhia, and Y.-M. Saint-Drenan, 2023: Impacts of the aerosol representation in WRF-Solar clear-sky irradiance forecasts over CONUS. J. Appl. Meteor. Climatol., 62, 227–250, https://doi.org/10.1175/JAMC-D-22-0059.1.</p> <p>Aerosol optical depth (AOD) is a primary source of solar irradiance forecast error in clear-sky conditions. Improving the accuracy of AOD in NWP models like WRF will thus reduce error in both direct normal irradiance (DNI) and global horizontal irradiance (GHI), which should improve solar power forecast errors, at least in cloud-free conditions. In this study clear-sky GHI and DNI was analyzed from four configurations of the WRF-Solar model with different aerosol representations: 1) The default Tegen climatology; 2) Imposing AOD forecasts from the GEOS-5 model; 3) Imposing AOD forecasts from the Copernicus Atmosphere Monitoring Service (CAMS) model; and 4) The Thompson-Eidhammer aerosol-aware water/ice-friendly aerosol climatology. Over eight months of these 15-min output forecasts are compared against high-quality irradiance observations at NOAA SURFRAD and Solar Radiation (SOLRAD) stations located across CONUS. In general, WRF-Solar with GEOS-5 AOD had the lowest errors in clear-sky DNI, while WRF-Solar with CAMS AOD had the highest errors, higher even than the two aerosol climatologies, which is consistent with validation of the four AOD550 datasets against AERONET stations. For clear-sky GHI, the statistics differed little between the four models, as expected due to the lesser sensitivity of GHI to aerosol loading. Hourly-average clear-sky DNI and GHI was also analyzed, and additionally compared with CAMS model output directly. CAMS irradiance performed competitively with the best WRF-Solar configuration (with GEOS-5 AOD). The markedly different performance of CAMS versus WRF-Solar with CAMS AOD indicates that CAMS is apparently less sensitive to AOD550 than WRF-Solar is.</p> <p>wrf_aeronet_aod550_by_cycle_20191119-20200730.nc:</p> <p>NetCDF, 317 MB</p> <p>WRF-Solar AOD550 spatially interpolated to AERONET stations and AERONET observed AOD550. Organized in arrays sorted by model cycle time (once daily at 09 UTC from 20191119 to 20200730) and model lead time (every 15 min to 45 h). The four WRF-Solar experiments correspond to the four from Lee et al. (2022).</p> <p>wrf_surfrad_solrad_inst_dni_ghi_by_cycle_20191119-20200730.nc:</p> <p>NetCDF, 102 MB</p> <p>WRF-Solar clear-sky DNI and clear-sky GHI interpolated to SURFRAD & SOLRAD stations, and SURFRAD & SOLRAD observed clear-sky DNI and clear-sky GHI. Organized in arrays sorted by model cycle time (once daily at 09 UTC from 20191119 to 20200730) and model lead time (every 15 min to 45 h). The four WRF-Solar experiments correspond to the four from Lee et al. (2022).</p> <p>wrf_surfrad_solrad_hrly_dni_ghi_by_cycle_20191119-20200730.nc:</p> <p>NetCDF, 26 MB</p> <p>WRF-Solar clear-sky DNI and clear-sky GHI interpolated to SURFRAD & SOLRAD stations, and SURFRAD & SOLRAD observed clear-sky DNI and clear-sky GHI. All values are averaged to time-ending 1-hourly averages. Organized in arrays sorted by model cycle time (once daily at 09 UTC from 20191119 to 20200730) and model lead time (every 1 h to 45 h). The four WRF-Solar experiments correspond to the four from Lee et al. (2022).</p> <p>cams_surfrad_solrad_hrly_dni_ghi_by_cycle_20191119-20200730.nc:</p> <p>NetCDF, 26 MB</p> <p>CAMS clear-sky DNI and clear-sky GHI interpolated to SURFRAD & SOLRAD stations, and SURFRAD & SOLRAD observed clear-sky DNI and clear-sky GHI. All values are averaged to time-ending 1-hourly averages. Organized in arrays sorted by model cycle time (once daily at 00 UTC from 20191119 to 20200730) and model lead time (every 1 h to 54 h).</p>
Matlab codes implementing the XDROM+ data-driven ENSO forecast model and some analysis of it
<p>This is the BEST forecast model of large scale features of ENSO as of today, beating (Zhao et al. Nature 2024).</p> <p>This archive is supplementary to a comment article concerning (Zhao et al. Nature 2024) intended as a "Matters Arising" piece to be submitted to Nature (https://www.researchsquare.com/article/rs-5336072/v1). Given that i criticise also the handling editor and 3 reviewers of (Zhao et al. Nature 2024) calling their incompetence out, do not be surprised if you have to look for the paper in some other journal instead. Oh well, integrity is above all else, no?! On that note, may I interest you in a bit of sci-fi? https://www.linkedin.com/pulse/crime-punishment-bit-differently-tamas-bodai-g4cvf/?trackingId=WdQkSNjgSuyxlyWVLRonrw%3D%3D</p>
ScienceDex guides
Understand access before you commit
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.