The cases included in this module demonstrate the unique abilities of POES data from both the AMSU and SSM/I instruments to supplement other observing and modeling systems. The first case used AMSU derived moisture imagery and data to augment traditional GOES imagery and AVN and Eta models to forecast snow totals. The following bullets describe specific points made in the module.
- Analysis of GOES WV imagery showed that some modifications to the model's depiction of upper level features and moisture were required.
- The AMSU TPW product provides a means of quantifying the total column water content while GOES observes mainly middle and upper level moisture features. GOES water vapor imagery should therefore never be used to quantify low-level moisture.
- The AMSU TPW product indicated where the models were less accurate in forecasting both timing and location of moisture, despite the presence of clouds.
- The AMSU TPW product provides forecasters with a vital tool to predict the location and timing (start/end) of significant precipitation events.
- The majority of the precipitation observed over eastern Washington and northern Idaho occurred in association with the plume of moisture shown by AMSU TPW. Meanwhile, the warmest temperatures on GOES WV imagery did not correlate well with the majority of the precipitation.
The second case demonstrated the TRaP method and product for predicting rainfall totals using POES microwave data for a hurricane event. The following bullets describe specific points made in the module.
- Georges' rainfall totals for 27-30 September compare reasonably well with short-term satellite-based microwave rainfall estimates made using the TRaP method.
- TRaP estimates can be generated using both SSM/I and AMSU rain rates.
- TRaP estimates provide another valuable tool in predicting the potential for heavy rainfall and flash flooding conditions in advance of tropical cyclones.