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\begin{document}

\title{On the Rainfall and Temperature Forecast Skill for a Tropical Andean
Mountain Area in Northern South America Using Different Operational
Weather Forecast Strategies: Role of the Diurnal Cycle of Rainfall on
the Success of Data Assimilation}



\author[1]{Gisel Guzmán}%
\author[1]{Lina Isabel Ceballos}%
\author[1]{Juan Camilo Hernández}%
\author[1]{Mauricio Zapata Henao}%
\author[1]{Manuela Guarin Vargas}%
\author[2]{Carlos David Hoyos Ortiz}%
\affil[1]{SIATA}%
\affil[2]{SIATA, Universidad Nacional de Colombia}%


\vspace{-1em}



  
  \date{May 25, 2024}


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\begin{abstract}
Numerical Weather Prediction models (NWP) have been used extensively
since the '40-'50s. Despite the advances in the field, the
representation and forecast of the magnitude and variability of tropical
processes in models is still a challenge. One of the steps to improve
the precipitation forecasts using limited-area models is to evaluate
which set of physical schemes and model domain configurations represent
in a better way the actual behavior observed in the tropics. We
implemented, as a part of a regional risk management strategy, two
different operational weather forecast strategies for a complex terrain
region in the Andes mountain range in northern South America. Both
strategies, together, generate a total of eleven different forecasts
every day, using the Weather Research and Forecasting model (WRF) with
initial and boundary conditions from the Global Forecast System (GFS).
The first configuration, implemented over five years ago and referred to
as SYNAPSIS, includes three nested domains (18, 6 and 2 km) and is
carried out every day using the 12 UTC GFS run and three different
microphysics parametrizations: Eta Ferrier scheme, Purdue Lin Scheme and
Thompson Scheme. The forecast lead-time of the latter strategy is 120
hours, and it does not use data assimilation. Since December of 2017, we
implemented a second configuration termed RDFS, with two nested domains
(12 and 2.4 Km), which carried out four times a day using the 00, 06, 12
and 18 UTC GFS runs. This configuration has a 30-hours lead time with
the Thompson microphysics scheme. In RDFS, two WRF forecast runs are
performed for each start hour, one assimilating weather radar
reflectivity and the other without assimilation as control run, for a
total of eight forecast runs daily. In this study, we assess the
rainfall and temperature forecasts for all the different configurations
using precipitation derived from reflectivity from weather radar, and
air temperature at 2m from a network of automatic weather stations. We
use 6 hourly and monthly skill scores (RMSE, BIAS, and Correlation
coefficient) to quantify the precipitation differences between the
SYNAPSIS and the RDFS configurations. To evaluate the impact of data
assimilation in the precipitation forecast, we aggregate the results in
a region within the inner domain, and then we calculate the average
precipitation forecast between 0 and 36 predicted hours for RDFS with
and without data assimilation. The results suggest a strong relationship
between the forecast start time and the improve of precipitation
forecast accuracy using data assimilation. The diurnal cycle of
precipitation in the study region has a minimum in the morning (12 UTC)
and a maximum in the afternoon (00 UTC) and during the night (09 UTC).
The correspondence between the forecast improvement using data
assimilation and the diurnal cycle of precipitation is likely due to the
amount of assimilated data. In order to quantify the precipitation
differences between the diffe%
\end{abstract}%



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