Evaluation of Rainfall Data Derived from TRMM Satellite, MM5 Model and Ground Observation using Sapatio-Temporal Analysis in Arid and Semi-Arid Mountainous Area

1. Precise estimates of rainfall in areas with complex geographical features in the field of
climatology, agricultural meteorology and hydrology is very important. TRMM satellite
is the first international effort to measure rainfall from space reliably (Smith, 2007).
Another set of data that has become available in recent years is the output of numerical
prediction models. Akter and Islam (2007) used MM5 model for weather prediction
especially for rainfall in Bangladesh. They compared MM5 outputs with 3B42RT
production of TRMM, rain gage and radar data and concluded that MM5 is reliable for
rainfall prediction. Ochoa et al. (2014) compared 3B42 product of TRMM with
simulated rainfall data by WRF model. Their results showed that TRMM data is more
applicable for presenting spatial distribution of annual rainfall. In addition to the
methods of statistical comparison, the similarity algorithm (Herzfeld & Merriam, 1990)
was also used in this study. This algorithm compares a large number of data
simultaneously, which can be in the form of maps or models output. In Iran, very few
studies have compared the output of numerical prediction models with TRMM products
of rainfall. The aim of this study was to evaluate and compare the rainfall data using
similarity algorithm for different locations and time periods in order to fill a gap in the
space-time data.
2. Material and The study area consisted of North Khorasan, Khorasan Razavi and South Khorasan
provinces in North East of Iran, which is geographically located between the longitudes
of 55 to 61 degrees and latitudes of 30 to 38 degrees. The climate of the area is arid and
semi arid. Total area is approximately 313000 square kilometers. In this study, three
types of data were used. Ground-based observations used from synoptic and rain-gauge
stations of Meteorology Organization. The seventh series products of TRMM 3B42 sensor containing three hours TRMM rainfall data with a spatial resolution of 0.25
degree were downloaded for free from the site of NASA. MM5 model outputs which
were in the form of images with a spatial resolution of 0.5× 0.5 degrees for the period of
2000-2010 were also obtained from NASA and NOAA .In this study, KED as a
geostatistical method was used to interpolate rainfall. For running geostatistics
algorithms, GS and ArcGIS software were used. Similarity algorithm was executed
for each grid point map and the similarity values were derived. After standardization by
calculating the similarity value for the entire study area, F network model for similar
map was created. In similarity algorithm, closest values to zero indicate a good
similarity between the input maps in a specific location and higher values indicate
weaker similarity. Standardization algorithms, similarity and analytical software
programming in MATLAB were performed for each grid point of the map.
3. RMSE values for MM5 model were higher in the warm months. The highest RMSE
values were obtained in late spring and early summer. This result proved that in the
summer, rainfall was predicted less accurately than in the cold months in winter. RMSE
values for TRMM showed a reverse pattern with MM5 model output. Maximum
amount of RMSE for TRMM was obtained in January with 14 mm per month. The
reason for this may be because microwave energy scattering from frozen ice on the
ground. The scattering from rain or frozen rain in the atmosphere is similar. Similarity
values in the area were scattered with uniform distribution that represents the least
significant inter-annual variation is cold seasons. For the warm seasons, in the south and
north of the area, similarity values vary from 1 to 2. Results showed that inter-annual
variations of rainfall in warm seasons and in central areas is high. One of the reasons for
these results can be errors in the observed data.
By examining the time series of TRMM images using similarity algorithm, we found
that in the cold season, the south zone of the study area had similarity values 0.05 to 0.1
with a uniform distribution of values. However, higher similarity values were obtained
for the northern and central areas where the distribution of similarity values was not
uniform.
Due to these facts, it can be concluded that rainfall production of TRMM data was
relatively good in the cold season in south and relatively week in north and central parts
of the region. In the warm season the least amount of similarity could be seen in the
northeast part of the study area. But generally, TRMM estimated rainfall fairly in the
warm season.
4. The validation results of MM5 model rainfall and TRMM monthly rainfall images
showed that the model predicted rainfall amounts in the cold months better than in the
warm months. However unlike the MM5 model, remote sensing images had the highest
error in cold months. The reason was the presence of snow and ice on the ground in the
cold months of winter. Considering inter-annual and seasonal changes, it became clear
that there is much difference between inter-annual remote sensing image changes and the actual amounts of rainfall (KED). Nevertheless the model inter-annual changes were
consistent with real data. Inter-annual changes of the model and the station data (KED)
were higher in cold season.
KED methods also retained spatial variability of rainfall as well as remote sensing data
and model output. The estimates, especially above 1500 meters in the central regions,
had low precision in the products. The results showed that in the absence of adequate
rain gages in the region, MM5 output model and TRMM data could be used to fill the
gaps.