TRMM and GPM: Radar Observations and Simulations with the Local Analysis and Prediction System (LAPS)

Tuesday, 15 December 2015: 08:30
3022 (Moscone West)
Steven C Albers1,2, Kirk Holub3 and Yuanfu Xie2, (1)Cooperative Institute for Research in the Atmosphere, Fort Collins, CO, United States, (2)NOAA, ESRL, Boulder, CO, United States, (3)NOAA, GSD, Boulder, CO, United States
The Local Analysis and Prediction System (LAPS), developed at NOAAs Earth System Research Laboratory is used for data assimilation, nowcasting, and model initialization/post-processing.
It is a portable system and typically runs with a high resolution and rapid update
Blends a wide variety of in-situ and remotely sensed data sets (e.g. METARs, mesonets, radar, satellite)‏
Here we test the assimilation of PMM radar data, using reflectivity obtained from the TRMM satellite, as a preparation for GPM. A
case study for July 26 2013 with a small region of convection occurring over Florida.

The 3-D LAPS domain is on a 1km grid and is producing analyses and forecasts.
TRMM radar data was remapped to mimic the appearance of reflectivity in a ground-based radar over Florida.
Three assimilation experiments are being performed using non-radar observations plus: TRMM radar,
ground-based radar, and neither source of radar. We are comparing both analyses (initial condition) and forecasts where the WRF model
is initialized with the LAPS analysis.

When evaluating the results we consider some big picture aspects in that
the GPM Core Observatory radar coverage is limited in space and time and potentially less operational model benefit.
To address this 4DVAR can help increase impact (particularly in a global model), since it spreads observations in time and space.
The spreading in time also helps compensate for latency of the real-time data stream.
We can also use GPM (core satellite) radar paired with microwave imager data to calibrate microwave data from other
GPM constellation satellites. We thus can leverage more frequent satellite microwave passes compared with radar to
assess hydrometeor climatological covariance between various species, fill in ice phase information.
These relationships, leveraged from related climate research, help to provide constraints for our planned variational analysis improvements.