> For the complete documentation index, see [llms.txt](https://docs.therisk.global/nexus-initiatives/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.therisk.global/nexus-initiatives/heatwaves-prediction/appendix-a-data-source/numerical-deterministic/gdps.md). # GDPS ### **1. Overview of GDPS v9.0.0** * **Implemented on June 11, 2024**, at the **Canadian Meteorological Centre (CMC)**. * Major **technical improvements** in **data assimilation**, minor updates to **atmospheric and ocean-ice forecasting**. * **Provides high-resolution global forecasts** at **15 km resolution** with **84 vertical levels**. * **Runs four times daily (00, 06, 12, and 18 UTC).** * **Uses a fully ensemble-derived 4DEnVar assimilation scheme**, making forecasts more accurate. * **Supports global applications**, including **extreme weather prediction, climate monitoring, and environmental modeling**. *** ### **2. Atmospheric Data Assimilation & Objective Analysis** The **data assimilation** process ensures **real-time atmospheric observations** are incorporated into the model to improve predictions. #### **2.1 Key Features** * **Model:** Version 5.2 of the **Global Environmental Multiscale (GEM) model**. * **Assimilation Method:** Uses a **Four-Dimensional Ensemble-Variational (4DEnVar) approach**. * **Combines variational data assimilation with ensemble-based background error covariances.** * **Hourly analysis increments** improve forecast accuracy. * **Observations are assimilated in 15-minute bins over a 6-hour window.** * Uses **Radiative Transfer for TOVS (RTTOV v13)** for processing satellite radiance data. * **Uses 25 km resolution ensembles from GEPS 8.0.0** for background error calculations. #### **2.2 Data Sources & Observations Assimilated** GDPS integrates data from various sources to refine its global forecasts: 1. **Satellite Radiance Data** * Microwave Sensors: **AMSU-A, MHS, ATMS, SSMIS, MWHS-2**. * Infrared Sensors: **AIRS, IASI, CrIS**. * Geostationary Imagers. 2. **Ground-Based & Other Observations** * **Radiosondes (weather balloons)** * **Surface stations (SYNOP, METAR, BUOY, SHIP)** * **Aircraft-based weather observations** * **GPS-based refractivity data** * **Atmospheric Motion Vectors (AMVs) from satellites** * **Scatterometer winds for oceanic forecasting** #### **2.3 Quality Control & Bias Corrections** * **Satellite Radiance Bias Correction** * **Observational bias is corrected based on last 7 days of analysis data.** * **Uses inter-channel correlation error matrix for radiance data processing.** * **Dynamic bias correction for GPS-based refractivity observations.** * **Other Enhancements** * **Improved use of radiosonde BUFR data.** * **Background-error covariances updated using a scale-dependent localization approach.** * **Incremental 3D-Var assimilation for ozone predictions.** *** ### **3. Forecast Model Configuration** #### **3.1 Key Features** * **Model:** GEM v5.2 * **Numerical Grid:** **Global coverage** at **15 km resolution**. * **84 vertical levels**, with **top level at 0.1 hPa**. * **Iterative-implicit semi-Lagrangian scheme** with a **450-second timestep**. * **Prognostic Variables Include:** * **3D winds, temperature, surface pressure, cloud condensate, ozone.** * **Precipitation, cloud cover, humidity, UV indices.** #### **3.2 Physical Parameterizations** * **Deep Convection:** Updated **Kain & Fritsch scheme** for high-resolution forecasting. * **Shallow Convection:** Bechtold mass-flux scheme. * **Microphysics:** Two-moment cloud microphysics scheme. * **Boundary Layer Mixing:** Based on **Turbulent Kinetic Energy (TKE)**. #### **3.3 Land Surface & Hydrology** * **Uses a four-layer surface model** for **land, water, sea ice, and glacier**. * **Surface variables include soil temperature, snow depth, sea ice thickness.** * **Coupled with ocean models for dynamic sea surface temperature updates.** *** ### **4. Ocean & Sea Ice Prediction System** GDPS includes a **coupled ocean-sea ice prediction component**, which enhances climate and marine forecasts. #### **4.1 Key Features** * **Ocean Model:** **NEMO 3.6 (Primitive Equations)** * **Sea Ice Model:** **CICE 6.2.0** * **Resolution:** **ΒΌ degree (\~25 km horizontal resolution) with 50 vertical levels**. * **Uses updated turbulence mixing models for accurate ocean forecasts.** * **Assimilates global sea ice and ocean observations (AVISO, CLS, CMC SST Analysis).** * **Sea ice thickness and fraction dynamically computed.** *** ### **5. Computational Performance** * **Runs on 3,600 cores**, completing **analysis in \~18 minutes**. * **Uses a nested-grid strategy for high-resolution sub-regional forecasts.** * **Parallel computing optimizations reduce processing time.** *** ### **6. Applications and Use Cases** GDPS **supports global decision-making** across multiple domains: * **Extreme Weather Forecasting:** Hurricanes, heatwaves, cold outbreaks. * **Climate Monitoring:** Long-term trends in temperature, precipitation, and ocean currents. * **Air Quality & Environmental Prediction:** Predicts air pollutants and wildfire smoke movement. * **Energy & Water Management:** Supports renewable energy integration by forecasting wind, solar, and hydrological conditions. * **Agriculture & Food Security:** Provides early warnings for droughts, frosts, and heat stress on crops. *** ### **7. Summary & Importance** The **GDPS v9.0.0** is a state-of-the-art **global weather prediction system** that significantly improves forecasting for **extreme weather events, environmental risks, and climate change adaptation**. With advanced **data assimilation techniques, high-performance computing, and an integrated ocean-sea ice model**, it serves as a critical tool for **scientists, policymakers, and emergency responders worldwide**. *** ### **8. References & Further Reading** * **Technical Note on GDPS-9.0.0:** [Link to official document](https://collaboration.cmc.ec.gc.ca/cmc/cmoi/product_guide/docs/tech_notes/technote_gdps-900_e.pdf) * **GDPS Data & Products Guide:** [Access here](https://eccc-msc.github.io/open-data/msc-data/nwp_gdps/readme_gdps_en/) * **Viewing Services for Forecast Products:** [MeteoCentre](http://www.meteocentre.com/plus) *** ### **Conclusion** GDPS **v9.0.0** represents a major leap in **global weather forecasting** capabilities. Its ability to **integrate real-time observational data** and **produce highly accurate multi-day forecasts** makes it a **powerful tool for climate resilience, disaster preparedness, and sustainable resource management**. {% embed url="" %} --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter: ``` GET https://docs.therisk.global/nexus-initiatives/heatwaves-prediction/appendix-a-data-source/numerical-deterministic/gdps.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.