224
(v) The Global Precipitation Climatology Centre (GPCC) dataset (Schneider
et al. 2013): ftp://ftp.dwd.de/pub/data/gpcc/html/gpcc_monitoring_v5_doi_
download.html
(vi) The Global Precipitation Climatology Project (GPCP) (Adler et al. 2003):
https://www.esrl.noaa.gov/psd/data/gridded/data.gpcp.html
(vii) Air temperature information from NCEP/NCAR (National Center for
Environmental Prediction /National Center for Atmospheric Research)
reanalysis data (Kistler et al. 2001): https://www.esrl.noaa.gov/psd/data/
gridded/data.ncep.reanalysis.html
(viii) ERA-Interim is the recent global atmospheric reanalysis data produced by the
European Centre for Medium Range Forecasts (ECMWF) (Dee et al. 2011):
https://apps.ecmwf.int/datasets/data/interim-full-mnth/levtype=sfc/
2.2 In-Situ Measurements
Snow and Avalanche Study Establishment (SASE), India has established manual
observatories at different climatic zones where snow-meteorological data is recorded
as per WMO guidelines. For evaluation of the gridded datasets, the wintertime mean
temperature and precipitation data of past 25 years (1991–2015) collected from 23
observatories as shown in Fig. 1 is employed. Here winter mean temperature refers
to mean of maximum and minimum temperature. It is to be noted that due to varying altitudes, NWH precipitation is a mixture of snow, sleet and rain; hence the
respective values were converted into water equivalents (mm).
3 Methods
The study area depicts non-uniform distribution of observatories (Fig. 1). For comparison purposes, the observed data were not interpolated/extrapolated to grids rather
grid points nearest to observatories were extracted to facilitate point to point comparison. However, since in-situ measurements for winter season were already processed for gaps filling and tested for data quality, validation was done for winter
periods only (Kanda et al. 2018). Here winter period refers to aggregate of 6 months,
i.e. November–April. The performance of gridded datasets was tested on two levels:
Amount and trends. Statistical measures which compared the relative performance of
datasets included Root Mean Squared Error (RMSE), Mean Absolute Error (MAE),
Pearson’s Correlation Coefficient (R) etc. It is to be noted that Kanda et al. (2019)
conducted this study for different elevation zones, i.e. E1, E2 and E3 of NWH. E1,
E2 and E3 of their study would be referred to as LH, GH and KH in this study since
majority of observatories in E1, E2 and E3 belong to LH, GH and KH respectively.
For study of spatio-temporal patterns and trends using datasets, annual averages
for temperature and annual cumulative values for precipitation were calculated.
Further linear trends (based on linear regression equation) were calculated for differH. S. Negi and N. Kanda
(v) The Global Precipitation Climatology Centre (GPCC) dataset (Schneider
et al. 2013): ftp://ftp.dwd.de/pub/data/gpcc/html/gpcc_monitoring_v5_doi_
download.html
(vi) The Global Precipitation Climatology Project (GPCP) (Adler et al. 2003):
https://www.esrl.noaa.gov/psd/data/gridded/data.gpcp.html
(vii) Air temperature information from NCEP/NCAR (National Center for
Environmental Prediction /National Center for Atmospheric Research)
reanalysis data (Kistler et al. 2001): https://www.esrl.noaa.gov/psd/data/
gridded/data.ncep.reanalysis.html
(viii) ERA-Interim is the recent global atmospheric reanalysis data produced by the
European Centre for Medium Range Forecasts (ECMWF) (Dee et al. 2011):
https://apps.ecmwf.int/datasets/data/interim-full-mnth/levtype=sfc/
2.2 In-Situ Measurements
Snow and Avalanche Study Establishment (SASE), India has established manual
observatories at different climatic zones where snow-meteorological data is recorded
as per WMO guidelines. For evaluation of the gridded datasets, the wintertime mean
temperature and precipitation data of past 25 years (1991–2015) collected from 23
observatories as shown in Fig. 1 is employed. Here winter mean temperature refers
to mean of maximum and minimum temperature. It is to be noted that due to varying altitudes, NWH precipitation is a mixture of snow, sleet and rain; hence the
respective values were converted into water equivalents (mm).
3 Methods
The study area depicts non-uniform distribution of observatories (Fig. 1). For comparison purposes, the observed data were not interpolated/extrapolated to grids rather
grid points nearest to observatories were extracted to facilitate point to point comparison. However, since in-situ measurements for winter season were already processed for gaps filling and tested for data quality, validation was done for winter
periods only (Kanda et al. 2018). Here winter period refers to aggregate of 6 months,
i.e. November–April. The performance of gridded datasets was tested on two levels:
Amount and trends. Statistical measures which compared the relative performance of
datasets included Root Mean Squared Error (RMSE), Mean Absolute Error (MAE),
Pearson’s Correlation Coefficient (R) etc. It is to be noted that Kanda et al. (2019)
conducted this study for different elevation zones, i.e. E1, E2 and E3 of NWH. E1,
E2 and E3 of their study would be referred to as LH, GH and KH in this study since
majority of observatories in E1, E2 and E3 belong to LH, GH and KH respectively.
For study of spatio-temporal patterns and trends using datasets, annual averages
for temperature and annual cumulative values for precipitation were calculated.
Further linear trends (based on linear regression equation) were calculated for differH. S. Negi and N. Kanda