> 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/hrdps.md). # HRDPS **Overview** The **High Resolution Deterministic Prediction System (HRDPS) v7.0.0** is an advanced numerical weather prediction (NWP) system implemented by Environment and Climate Change Canada (ECCC) under the Meteorological Service of Canada (MSC). The HRDPS operates as part of a limited-area model (LAM) configuration within the **Global Environmental Multiscale (GEM) model** framework. **Key Features:** * Provides high-resolution forecasts at **2.5 km spatial resolution** with **62 vertical levels**. * Uses a **4D-EnVar data assimilation approach** for improved atmospheric analysis. * Generates forecasts **four times daily** with **short-term prediction cycles every six hours**. * Supports early-warning systems for **severe weather phenomena** such as storms and heatwaves. * Data is **continuously assimilated**, improving forecast accuracy for **Days 1 and 2**. *** ### **1. Data Assimilation and Objective Analysis** Data assimilation is the process of integrating observational data into a numerical model to improve forecasts. The HRDPS v7.0.0 follows a **continuous upper-air cycling strategy** to refine predictions. #### **1.1 Assimilation Process** * Uses **4D-EnVar (Four-Dimensional Ensemble-Variational Data Assimilation)**, which **combines ensemble forecasting with variational data assimilation** to improve accuracy. * **Observations are assimilated every 6 hours (00, 06, 12, 18 UTC) with a 7-hour observation cut-off** to ensure real-time updates. * Uses a **Gaussian grid (\~25 km horizontal resolution)** to process atmospheric data. * Incorporates **radiance data from satellite sensors** using advanced radiative transfer models. #### **1.2 Data Sources and Observations** HRDPS ingests a variety of observational data: 1. **Satellite Radiance Data** * Microwave sensors (e.g., AMSU-A, MHS, ATMS). * Infrared sensors (e.g., AIRS, IASI, CrIS). * Geostationary imagers. 2. **Ground-Based Observations** * **Radiosondes (weather balloons)** provide **temperature, pressure, and humidity profiles**. * **Surface weather stations** supply real-time **temperature, wind, and pressure readings**. * **Aircraft-based observations** enhance vertical profiles. 3. **Radar and GPS Data** * **Radar reflectivity** helps estimate precipitation rates using **Latent Heat Nudging (LHN)**. * **GPS-based refractivity data** aids in atmospheric moisture profiling. *** ### **2. Forecast Model Configuration** The forecasting component of HRDPS 7.0.0 relies on **Version 5.2.0 of the GEM model**. #### **2.1 Model Formulation** * **Numerical Grid:** Covers **Canada, parts of the northern U.S., and adjacent oceans**, with a **2.5 km horizontal resolution**. * **Time Integration:** **Semi-Lagrangian numerical scheme**, using a **60-second timestep** for efficient computation. * **Physical Parameterizations:** * **Deep convection:** Kain & Fritsch scheme. * **Shallow convection:** Kuo Transient scheme. * **Cloud and precipitation microphysics:** P3 (two-moment scheme). * **Turbulence & boundary-layer mixing:** Based on the **Turbulent Kinetic Energy (TKE) approach**. #### **2.2 Land Surface and Hydrology** * **Soil temperature and moisture** are provided by the **Canadian Land Data Assimilation System (CaLDAS)** at 2.5 km resolution. * **Sea surface temperature and sea-ice data** are obtained from the **Global Deterministic Prediction System (GDPS-9.0.0)**. *** ### **3. Lateral Boundary Conditions and Initial Surface Conditions** HRDPS **requires accurate initial and boundary conditions** for reliable forecasting. * **Lateral Boundary Conditions (LBCs)** come from the **GDPS-9.0.0**, updated **hourly at 10 km resolution**. * **Initial Surface Conditions (ISC)** are sourced from the **CaLDAS assimilation system**. *** ### **4. Computational Performance** * The **data assimilation step runs on 3,600 computing cores**, completing in **\~13 minutes per cycle**. * The forecast model uses a **nested-grid approach** with finer resolution over key areas. *** ### **5. Applications and Impact** HRDPS is **critical for operational weather forecasting in Canada**. It supports: * **Short-term severe weather prediction** (storms, heatwaves, floods). * **Air quality and wildfire smoke forecasting**. * **Energy grid demand prediction**, especially for extreme heat and cold. * **Agriculture and hydrology**, improving water resource management. *** ### **6. Summary and Importance** HRDPS **v7.0.0** is a **state-of-the-art, high-resolution weather forecasting system** that significantly improves Canada’s ability to predict and respond to severe weather events. By using **advanced data assimilation, high-performance computing, and real-time observational data**, it enhances forecasting for disaster resilience and climate adaptation. {% 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: ``` GET https://docs.therisk.global/nexus-initiatives/heatwaves-prediction/appendix-a-data-source/numerical-deterministic/hrdps.md?ask= ``` The question should be specific, self-contained, and written in natural language. 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.