Assessing the use the Canadian Precipitation Analysis Data for Monitoring and Modelling the Impacts of Weather on Agricultural Production
Abstract
Timely information on weather and climate conditions and risks are of great importance to the agricultural sector. Monitoring the impacts on water, soil and agricultural production is essential for a region's ability to adapt, resist, and recover from droughts, floods, or other extremes. Agroclimate information is essential for producers and decision-makers to help manage risks. Traditionally, these impacts have been assessed using station based meteorological data, but in many remote and rural areas of Canada, weather station densities are inadequate for many agricultural models, including high resolution yield forecasting. Since 2011, Environment and Climate Change Canada (ECCC) began producing a precipitation analysis data set that provides objective estimates of precipitation amounts at any given location in Canada, based on regional forecast models with integration of observations, including meteorological stations and ground based radar. The precipitation analysis from the CaPA system linked to the Canadian Land Data Assimilation System (CaLDAS) developed by the Meteorological Research Division (MRD) of ECCC. CaLDAS provides the information on soil moisture, soil temperature and snowpack across Canada required as initial conditions by MSC's numerical weather prediction (NWP) systems such as the High Resolution Deterministic Prediction System (HRDPS) at 2.5 km, allowing higher resolution precipitation estimates that get closer to the optimal spatial scales for agricultural monitoring and modelling. This research will examine the accuracy of agricultural indices and models derived from regional and high resolution precipitation analyses data sets to determine improvements and trade offs in accuracy and spatial coverage. Analysis data sets were assessed against quality controlled station based estimates and derived agroclimate indices. Preliminary results show some differences in precipitation estimates between the data sets, particularly in coastal and northern regions. The overall spatial trends in agroclimate indices between the station and analysis data sets was consistent. The importance of consistent long term data sets is clear for deriving agroclimatic normals.
- Publication:
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AGU Fall Meeting Abstracts
- Pub Date:
- December 2019
- Bibcode:
- 2019AGUFMGC41H1242P
- Keywords:
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- 1616 Climate variability;
- GLOBAL CHANGE;
- 1640 Remote sensing;
- GLOBAL CHANGE;
- 1807 Climate impacts;
- HYDROLOGY;
- 6309 Decision making under uncertainty;
- POLICY SCIENCES