Latent heating profiles from GOES-16 and its impacts on precipitation forecasts

Abstract. Latent heating (LH) is an important factor in both
weather forecasting and climate analysis, being the essential factor
affecting both the intensity and structure of convective systems. Yet,
inferring LH rates from our current observing systems is challenging at
best. For climate studies, LH has been retrieved from the precipitation
radar on the Tropical Rainfall Measuring Mission (TRMM) using model
simulations in a lookup table (LUT) that relates instantaneous radar data
to corresponding heating profiles. These radars, first on TRMM and then the
Global Precipitation Measurement Mission (GPM), provide a continuous record
of LH. However, the temporal resolution is too coarse to have significant
impacts on forecast models. In operational forecast models such as
High-Resolution Rapid Refresh (HRRR), convection is initiated from LH
derived from ground-based radars. Despite the high spatial and temporal
resolution of ground-based radars, their data are only available over
well-observed land areas. This study develops a method to derive LH from the
Geostationary Operational Environmental Satellite-16 (GOES-16) in near-real
time. Even though the visible and infrared channels on the Advanced Baseline
Imager (ABI) provide mostly cloud top information, rapid changes in cloud
top visible and infrared properties, when formulated as an LUT similar to
those used by the TRMM and GPM radars, can successfully be used to derive LH
profiles for convective regions based on model simulations with a convective
classification scheme and channel 14 (11.2 µm) brightness temperatures.
Convective regions detected by GOES-16 are assigned LH profiles from a
predefined LUT, and they are compared with LH used by the HRRR model and one
of the dual-frequency precipitation radar (DPR) products, the Goddard
convective–stratiform heating (CSH). LH obtained from GOES-16 shows similar
magnitude to LH derived from the Next Generation Weather Radar (NEXRAD)
and CSH, and the vertical distribution of LH is also very similar with CSH.
A three-month analysis of total LH from convective clouds from GOES-16 and
NEXRAD shows good correlation between the two products. Finally, LH profiles
from GOES-16 and NEXRAD are applied to WRF simulations for convective
initiation, and their results are compared to investigate their impacts on
precipitation forecasts. Results show that LH from GOES-16 has similar
impacts to NEXRAD in terms of improving the forecast. While only a proof of concept,
this study demonstrates the potential of using LH derived from GOES-16 for
convective initialization.
