The 20th Northeast Regional Operational Workshop (NROW XX): How scientific research translates into better forecasts

This past week, I attended the Northeast Regional Operational Workshop, held here in Albany, New York. This conference is a local meeting, organized by forecasters at the National Weather Service in Albany, New York, which had its genesis 20 years ago as a means for forecasters at the NWS, researchers and academics at the University at Albany, and others to share results of research projects which relate directly to forecast operations in the National Weather Service. Today, the conference is attended by forecasters from many National Weather Service offices, students and faculty from the University at Albany, local media meteorologists, and representatives from the private sector such as myself.

One of the best things about being a meteorologist in Albany, New York is the great collaboration that exists between the University and the National Weather Service. I personally benefited from this as a student, having had the opportunity to work as an undergraduate intern at the NWS office, and later work directly with forecasters on applied research projects in graduate school. Good forecasters pay close attention to observations, and the skills I learned through those experiences serve me well as a forensic meteorologist today.

Dr. Jerry Brotzge, director of the New York State Mesonet, gave a presentation which detailed the many uses of the large volume of observations which is collected daily at sensors located all across the state. This dataset is becoming invaluable for us in the forensic meteorology sector, as it provides much better coverage than the standard automated surface observing stations which are commonly located at airports. The ability to have five-minute observations of temperature, precipitation, wind speed, and other parameters, as well as webcam footage at each site, has allowed us to determine weather conditions at remote locations to a much greater degree of certainty than ever before.

Weather data from a NYS Mesonet station in Bronx, New York.

Dr. Lance Bosart gave a talk about a particular snowstorm which had extensive public impact down in the New York metropolitan region. The storm was poorly handled by the forecast models (although there were signals in the observational data that the models did not pick up on), which then translated into difficult messaging for forecasters to inform the public of the appropriate hazards. The storm began after lunch, and many folks decided to leave early to ‘beat the storm’ home. However, that resulted in gridlock on roadways as snow quickly became heavy while so many were on the roads.

One of the key questions meteorologists seek to answer is how to distill the massive volume of information we receive from observations and models into a clear message to the public that translates into appropriate actions to minimize loss of life, injuries, and property damage. It is not a simple question and involves collaboration between social scientists, meteorologists, emergency managers, city officials, and many others. In many ways, communication of the forecast (and the forecast uncertainties!) is equally as important as informing people how to respond to that information.

Traffic map from the NYC area from November 15, 2018.

Another important piece of knowledge that was shared was the current state of the suite of computer models which are used every day by the NWS and other forecasters. The forecast models have undergone some significant updates and changes during the last year, which will affect the forecast output. Prior to using the new model updates, studies are conducted using both the old and the new in parallel to identify any potential problems that need to be corrected prior to making the switch and retiring the old model. Here, the NWS Albany Science Operations Officer, Mike Evans, is giving an update on that process using a specific weather event as a case example.

Comparison of existing model data to the new forecast model.

Several talks were given which summarized results and findings from the Ontario Winter Lake effect Systems (OWLeS) field study. The field project collected data from the lake effect snow belt around Lake Ontario, and the results will be used to improve understanding of what causes lake effect snow bands to take on certain characteristics (such as single-band vs. multi-band), how the nearby terrain effects snow bands once they set up, and will ultimately result in better forecasting of these localized but very high impact events. One really cool piece of equipment which was used in this study is something called a ‘Parsivel disdrometer’. A laser beam in between each end of this unit is able to measure the size and fall speed of snowflakes!

Parsivel disdrometer.

We at Shade Tree Meteorology are very fortunate to be located in a region which has such a high concentration of meteorologists who work in forecasting, research, academia, and the private sector. The main focus of operational research, such as what was presented at NROW, is on increasing scientific understanding with the goal of achieving measurable, practical results, including better forecasts, better communication of those forecasts, and ultimately, prepared and resilient communities. This requires collaboration and discussion from all disciplines of meteorology, and local conferences such as NROW help to make that possible.

Leave a Reply

Your email address will not be published.