Satellite Program at NWS Charleston, SC NPOESS/GOES-R Curriculum Development Workshop Frank Alsheimer and Jon Jelsema.

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Presentation transcript:

Satellite Program at NWS Charleston, SC NPOESS/GOES-R Curriculum Development Workshop Frank Alsheimer and Jon Jelsema

Satellite Uses – Fire Weather Myrtle Beach, SC Wildfire April 23, 2009 Southeast, GA Wildfires April/May 2007

Key Future Satellite Requirements Latency - Current availability is not always adequate to meet operational requirements (this is mainly true of polar orbiter imagery and data). - Increasing availability is necessary for the data to be of consistent use to the operational forecasters. AWIPS Availability - Some of the useful data is currently unavailable in AWIPS (the primary tool used by the operational meteorologist to issue forecasts, watches, warnings and advisories). - Plans must be made to incorporate new satellite products in AWIPS if the NWS is going to benefit from the increase in Satellite Capabilities. Latency - Current availability is not always adequate to meet operational requirements (this is mainly true of polar orbiter imagery and data). - Increasing availability is necessary for the data to be of consistent use to the operational forecasters. AWIPS Availability - Some of the useful data is currently unavailable in AWIPS (the primary tool used by the operational meteorologist to issue forecasts, watches, warnings and advisories). - Plans must be made to incorporate new satellite products in AWIPS if the NWS is going to benefit from the increase in Satellite Capabilities.

Future Satellite Requirements Cont. Ease of AWIPS interrogation - Currently, it is a several step process to get POES and GOES sounder data. It should be a click-and-go process. - Further, there needs to be an interactive sampling capability (similar to the way today’s environmental standard data package and pop-up SkewT works). This is especially true of the GOES sounder data which is at least somewhat regular. Quick turnaround from research to operations - Right now, it can be a very cumbersome and time consuming process to get new data onto the AWIPS data feed (OSIP). While we have used the LDM as a backdoor for some products, this should not have to be SOP. - GOES proving ground and AWIPS2 (AWIPSNEXT) could be very beneficial in this arena BANDWIDTH Ease of AWIPS interrogation - Currently, it is a several step process to get POES and GOES sounder data. It should be a click-and-go process. - Further, there needs to be an interactive sampling capability (similar to the way today’s environmental standard data package and pop-up SkewT works). This is especially true of the GOES sounder data which is at least somewhat regular. Quick turnaround from research to operations - Right now, it can be a very cumbersome and time consuming process to get new data onto the AWIPS data feed (OSIP). While we have used the LDM as a backdoor for some products, this should not have to be SOP. - GOES proving ground and AWIPS2 (AWIPSNEXT) could be very beneficial in this arena BANDWIDTH

Training Ideas It is important to keep in mind that operational forecasters at an NWS WFO are constantly bombarded with information from satellite, radar, mesonets, unofficial observations, oodles of model solution data (both deterministic and ensemble), etc. It can become a data management nightmare. To get forecasters to use any particular set of data, it must: –Be easily available –Be understandable –Be accurate –Be reliable –Be proven superior to (or at least equal to) other options for the problem at hand

Training Ideas (cont.) Prior to Data Availability –The science behind the “new and improved” data –High resolution simulations presenting the “look” of new data images in our AWIPS systems –Examples of improvement in models due to data inclusion (a seemingly large gap currently exists). –Examples of current data gaps and how the new data will help fill those gaps After Data is Available –Case studies and simulations on AWIPS/WES covering situations where the use or addition of satellite data led to improved decision-making by forecasters. Current DLAC2 is a good example.