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of getting an estimate of system uniform over a grid (Bosilovich et al. 2008).
Examples of reanalysis datasets include European Centre for Medium-Range
Weather Forecasts Reanalysis-Interim (ERA-I) (Dee et al. 2011), National Center
for Environmental Prediction (NCEP) and the National Center for Atmospheric
Research (NCAR) (NCEP-NCAR-Reanalysis; (Kalnay et al. 1996) etc. Various factors which determine the fidelity of reanalysis datasets include its spatial resolution,
data assimilation efficacy and accurate representation of land-surface processes
(Forsythe et al. 2015). The above-mentioned strengths and weaknesses of various
datasets underpin the need for their evaluation prior to their use in areas like
Himalaya where climate is greatly influenced by orographic reliefs.
Various researchers in the past have already attempted to compare the performance
of gridded datasets over Himalayan region. Andermann et al. (2011) investigated the
performance of gridded datasets over Himalayan front and suggested the utility of
APHRODITE. Palazzi et al. (2013) compared few datasets and ERA-I was found to
be a reliable dataset. Dahri et al. (2016) also suggested the use of ERA-I for reconciling precipitation in high Himalaya. Hussain et al. (2017) evaluated APHRODITE and
TMPA products for Hindukush Karakoram Himalaya (HKKH) and found poor correlation of ground observatories with TMPA. However, no such study has been conducted for Indian Himalaya that too making use of long term (~25 years) and very
high altitude (2000–6000 m) observations which make this study unique.
Given the importance of dataset selection, we aim to assess the performance of
various datasets in reproducing climatic patterns and long-term trends over different
climatic zones of North-western Himalaya by making use of in-situ observations.
The evaluation of datasets has already been carried out by Kanda et al. (2019) for
winter season. However, present study further facilitates the appraisal of spatial and
temporal patterns of climate through two selected datasets. The temporal patterns
and trends have been studied for long term (1985–2015) and short term (pre and
post year 2000). Short term trends especially post 2000 have been incorporated
because only after year 2000, world has witnessed a surge in cryosphere mapping/
monitoring studies owing to advancements in satellite sensor technology and free
availability of data.
2 Study Area and Data
Northwest-Himalaya has been selected as study area (Fig. 1). This area is highly
diverse by virtue of significant altitudinal difference of mountain ranges it encompasses. Based on the snow avalanche climatology, this area has been divided into
three major zones, i.e. Lower Himalaya (LH) wherein the Pir Panjal and Shamshawari
ranges lie, Middle Himalaya encompassing the Great Himalaya (GH) range and
Upper Himalaya wherein Karakoram Himalaya (KH) lies (Sharma and Ganju
2000). The precipitation dynamics of these zones differ due to differential interaction of westerlies (Negi et al. 2018). The altitudes at these zones vary between 2000
and 6000 m.
An Appraisal of Spatio-Temporal Characteristics of Temperature and Precipitation…
of getting an estimate of system uniform over a grid (Bosilovich et al. 2008).
Examples of reanalysis datasets include European Centre for Medium-Range
Weather Forecasts Reanalysis-Interim (ERA-I) (Dee et al. 2011), National Center
for Environmental Prediction (NCEP) and the National Center for Atmospheric
Research (NCAR) (NCEP-NCAR-Reanalysis; (Kalnay et al. 1996) etc. Various factors which determine the fidelity of reanalysis datasets include its spatial resolution,
data assimilation efficacy and accurate representation of land-surface processes
(Forsythe et al. 2015). The above-mentioned strengths and weaknesses of various
datasets underpin the need for their evaluation prior to their use in areas like
Himalaya where climate is greatly influenced by orographic reliefs.
Various researchers in the past have already attempted to compare the performance
of gridded datasets over Himalayan region. Andermann et al. (2011) investigated the
performance of gridded datasets over Himalayan front and suggested the utility of
APHRODITE. Palazzi et al. (2013) compared few datasets and ERA-I was found to
be a reliable dataset. Dahri et al. (2016) also suggested the use of ERA-I for reconciling precipitation in high Himalaya. Hussain et al. (2017) evaluated APHRODITE and
TMPA products for Hindukush Karakoram Himalaya (HKKH) and found poor correlation of ground observatories with TMPA. However, no such study has been conducted for Indian Himalaya that too making use of long term (~25 years) and very
high altitude (2000–6000 m) observations which make this study unique.
Given the importance of dataset selection, we aim to assess the performance of
various datasets in reproducing climatic patterns and long-term trends over different
climatic zones of North-western Himalaya by making use of in-situ observations.
The evaluation of datasets has already been carried out by Kanda et al. (2019) for
winter season. However, present study further facilitates the appraisal of spatial and
temporal patterns of climate through two selected datasets. The temporal patterns
and trends have been studied for long term (1985–2015) and short term (pre and
post year 2000). Short term trends especially post 2000 have been incorporated
because only after year 2000, world has witnessed a surge in cryosphere mapping/
monitoring studies owing to advancements in satellite sensor technology and free
availability of data.
2 Study Area and Data
Northwest-Himalaya has been selected as study area (Fig. 1). This area is highly
diverse by virtue of significant altitudinal difference of mountain ranges it encompasses. Based on the snow avalanche climatology, this area has been divided into
three major zones, i.e. Lower Himalaya (LH) wherein the Pir Panjal and Shamshawari
ranges lie, Middle Himalaya encompassing the Great Himalaya (GH) range and
Upper Himalaya wherein Karakoram Himalaya (KH) lies (Sharma and Ganju
2000). The precipitation dynamics of these zones differ due to differential interaction of westerlies (Negi et al. 2018). The altitudes at these zones vary between 2000
and 6000 m.
An Appraisal of Spatio-Temporal Characteristics of Temperature and Precipitation…