Search

Although the quality of weather forecasts in the polar regions is improving, forecast skill there still lags behind lower latitudes. So far there have been relatively few efforts to evaluate processes in numerical weather prediction systems using in situ and remote sensing datasets from meteorological observatories in the terrestrial Arctic and Antarctic compared to the mid-latitudes. Progress has been limited both by the heterogeneous nature of observatory and forecast data and by limited availability of the parameters needed to perform process-oriented evaluation in multi-model forecast archives. The Year of Polar Prediction (YOPP) site Model Inter-comparison Project (YOPPsiteMIP) is addressing this gap by producing merged observatory data files (MODFs) and merged model data files (MMDFs), bringing together observations and forecast data at polar meteorological observatories in a format designed to facilitate process-oriented evaluation. An evaluation of forecast performance was performed at seven Arctic sites, focussing on the first YOPP Special Observing Period in the Northern Hemisphere (NH-SOP1) in February and March 2018. It demonstrated that although the characteristics of forecast skill vary between the different sites and systems, an underestimation in boundary layer temperature variability across models, which goes hand in hand with an inability to capture cold extremes, is a common issue at several sites. It is found that many models tend to underestimate the sensitivity of the 2 m air temperature (T2m) and the surface skin temperature to variations in radiative forcing, and the reasons for this are discussed. [ABSTRACT FROM AUTHOR]

A large and ever-growing body of geophysical information is measured in campaigns and at specialized observatories as a part of scientific expeditions and experiments. These collections of observed data include many essential climate variables (as defined by the Global Climate Observing System) but are often distinguished by a wide range of additional non-routine measurements that are designed to not only document the state of the environment but also the drivers that contribute to that state. These field data are used not only to further understand environmental processes through observation-based studies but also to provide baseline data to test model performance and to codify understanding to improve predictive capabilities. To address the considerable barriers and difficulty in utilizing these diverse and complex data for observation–model research, the Merged Observatory Data File (MODF) concept has been developed. A MODF combines measurements from multiple instruments into a single file that complies with well-established data format and metadata practices and has been designed to parallel the development of corresponding Merged Model Data Files (MMDFs). Using the MODF and MMDF protocols will facilitate the evolution of model intercomparison projects into model intercomparison and improvement projects by putting observation and model data "on the same page" in a timely manner. The MODF concept was developed especially for weather forecast model studies in the Arctic. The surprisingly complex process of implementing MODFs in that context refined the concept itself. Thus, this article explains the concept of MODFs by providing details on the issues that were revealed and resolved during that first specific implementation. Detailed instructions are provided on how to make MODFs, and this article can be considered a MODF creation manual. [ABSTRACT FROM AUTHOR]

Although the quality of weather forecasts in the polar regions is improving, forecast skill there still lags the lower latitudes. So far there have been relatively few efforts to evaluate processes in Numerical Weather Prediction systems using in-situ and remote sensing datasets from meteorological observatories in the terrestrial Arctic and Antarctic, compared to the mid-latitudes. Progress has been limited both by the heterogeneous nature of observatory and forecast data but also by limited availability of the parameters needed to perform process-oriented evaluation in multi-model forecast archives. The YOPP site Model Inter-comparison Project (YOPPsiteMIP) is addressing this gap by producing Merged Observatory Data Files (MODFs) and Merged Model Data Files (MMDFs), bringing together observations and forecast data at polar meteorological observatories in a format designed to facilitate process-oriented evaluation. An evaluation of forecast performance was performed at seven Arctic sites, focussing on the first YOPP Special Observing Period in the Northern Hemisphere (SOP1), February and March 2018. It demonstrated that although the characteristics of forecast skill vary between the different sites and systems, an underestimation in boundary layer temperature variance across models, which goes hand in hand with an inability to capture cold extremes, is a common issue at several sites. Diagnostic analysis using surface fluxes suggests that this is at least partly related to insufficient thermal representation of the land-surface in the models, which all use a single layer snow model. [ABSTRACT FROM AUTHOR]

A large and ever-growing body of geophysical information is measured on campaigns and at specialized observatories as a part of scientific expeditions/experiments. These collections of observed data include many essential climate variables (as defined by the World Meteorological Organization), but are often distinguished by a wide range of additional non-routine measurements that are designed to not only document the state of the environment, but also the drivers that contribute to that state. These field data are not only used to further understand the environmental processes through observation-based studies, but also to provide baseline data to test model performance and to codify understanding to improve predictive capabilities. To address the considerable barriers and difficulty in utilizing these diverse and complex data for observation-model research, the Merged Observatory Data File (MODF) concept has been developed. The MODF combines measurements from multiple instruments into a single file that complies with well-established data format and metadata practices and has been designed to parallel development of corresponding Merged Model Data Files (MMDFs). Using MODF and MMDF protocols will facilitate the evolution of Model Intercomparison Projects into Model Intercomparison and Improvement Projects by putting observation and model data 'on the same page' in a timely manner. The MODF concept was developed especially for weather forecast model studies in the Arctic. The surprisingly complex process of implementing MODFs in that context refined the concept itself. Thus this article explains the concept of MODFs by providing details on the issues that were revealed and resolved during this first specific implementation. Detailed instructions are provided on how to make MODFs, and this article can thus be considered a MODF creation manual. [ABSTRACT FROM AUTHOR]

ABSTRACT The Canadian Airport Nowcasting Project (CAN-Now) has developed an advanced prototype all-season weather forecasting and nowcasting system that can be used at major airports. This system uses numerical model data, pilot reports, ground in situ sensor observations (precipitation, icing, ceiling, visibility, winds), on-site remote sensing (such as vertically pointing radar and microwave radiometer) and off-site remote sensing (satellite and radar) information to provide detailed nowcasts out to approximately 6 h. The nowcasts, or short term weather forecasts, should allow decision makers such as pilots, dispatchers, de-icing crews, ground personnel or air traffic controllers to make plans with increased margins of safety and improved efficiency. The system has been developed and tested at Toronto Pearson International Airport (CYYZ) and Vancouver International Airport (CYVR). A Situation Chart has been developed to allow users to have a high glance value product which identifies significant weather related problems at the airport. New products combining observations and numerical model output into nowcasts have been tested. Some statistical verifications of forecast products, with comparisons to persistence, covering both a winter (2009/2010) and summer (2010) period have been made. Problems with the prediction of relative humidity and wind direction are outlined. The ability to forecast categorical variables such as ceiling, visibility, as well as precipitation rate and type accurately are discussed. Overall, for most variables, the nowcast systems can outperform persistence after the first 1 or 2 h, and provide more accurate forecasts than individual Numerical Weather Prediction models out to 6 h. [ABSTRACT FROM AUTHOR]