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Figure 5 – uploaded by Krishna Osuri

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Figure 5. Relation between forcing error and LDAS output error. The top row presents scatter diagram of LDAS soil moisture (m* m~*) error with error in temperature at 2 m (°C) obtained from a) Ex1, b) Ex2, c) Ex3, d) Ex4, e) Ex5. (f-j) and (k-o) are same as (a-e), but for surface downward long wave (W m~’), and short wave (W m~’) radiation respectively. (p-ad) is same as (a—-o) but for LDAS soil temperature (°C). All the relationship is calculated at 95% confidence level. The figure is generated using MATLAB 2015a, www. mathworks.com.

Figure 5 Relation between forcing error and LDAS output error. The top row presents scatter diagram of LDAS soil moisture (m* m~*) error with error in temperature at 2 m (°C) obtained from a) Ex1, b) Ex2, c) Ex3, d) Ex4, e) Ex5. (f-j) and (k-o) are same as (a-e), but for surface downward long wave (W m~’), and short wave (W m~’) radiation respectively. (p-ad) is same as (a—-o) but for LDAS soil temperature (°C). All the relationship is calculated at 95% confidence level. The figure is generated using MATLAB 2015a, www. mathworks.com.

Related Figures (11)

Figure 1. Observational station network used over the study domain and verification of diurnal variation of surface
Figure 1. Observational station network used over the study domain and verification of diurnal variation of surface
Table 2. Data used in developing the SM/ST fields using the LDAS.
Table 2. Data used in developing the SM/ST fields using the LDAS.
Figure 2. Verification of surface forcing parameters from different reanalysis products at in-situ station observations (shown in Fig. 1) for the period 2011-2013. Top row represents scatter diagram of temperature at 2m (°C) obtained from (a) MERRA, (b) MERRA2, (c) NCEP-CFSR, (d) ERA-Interim, and (e) GLDAS. Solid gray lines show linear regression fits to the data. (f-j), (k-o) and (p-t) are same as (a-e) but for surface pressure (hPa), surface downward long wave (W m~’) and short wave (W m~’) radiation respectively. The figure is generated using MATLAB 2015a, www.mathworks.com.
Figure 2. Verification of surface forcing parameters from different reanalysis products at in-situ station observations (shown in Fig. 1) for the period 2011-2013. Top row represents scatter diagram of temperature at 2m (°C) obtained from (a) MERRA, (b) MERRA2, (c) NCEP-CFSR, (d) ERA-Interim, and (e) GLDAS. Solid gray lines show linear regression fits to the data. (f-j), (k-o) and (p-t) are same as (a-e) but for surface pressure (hPa), surface downward long wave (W m~’) and short wave (W m~’) radiation respectively. The figure is generated using MATLAB 2015a, www.mathworks.com.
Figure 3. Comparison of error statistics of the surface forcing parameters from different reanalysis products at in-situ station observations (shown in Fig. 1) for the period 2011-2013. The first column represents number of stations per bin of (a) correlation (r), (b) root mean square error (RMSE), (c) mean bias (BIAS) and (d) the ratio of the standard deviation of the reanalysis product o to that from the observations 6455 (O;atio)s for temperature at 2m (°C). (e-h), (i-l) and (m-p) are same as (a-d), but for surface pressure (hPa), surface downward short wave (W m7 *), and long wave (W m ~”) radiation respectively. The figure is generated using MATLAB 2015a, www.mathworks.com. Figure 3. Comparison of error statistics of the surface forcing parameters from different reanalysis Table 3. Parameter values for linear regression fits (y = ax + b), correlation coefficient (r) and root mean square error (RMSE) for T2 (°C), PSFC (hPa), SWRAD (W m7”) and LWRAD (W m7”) obtained from each analysis/forecast products to in-situ observations.
Figure 3. Comparison of error statistics of the surface forcing parameters from different reanalysis products at in-situ station observations (shown in Fig. 1) for the period 2011-2013. The first column represents number of stations per bin of (a) correlation (r), (b) root mean square error (RMSE), (c) mean bias (BIAS) and (d) the ratio of the standard deviation of the reanalysis product o to that from the observations 6455 (O;atio)s for temperature at 2m (°C). (e-h), (i-l) and (m-p) are same as (a-d), but for surface pressure (hPa), surface downward short wave (W m7 *), and long wave (W m ~”) radiation respectively. The figure is generated using MATLAB 2015a, www.mathworks.com. Figure 3. Comparison of error statistics of the surface forcing parameters from different reanalysis Table 3. Parameter values for linear regression fits (y = ax + b), correlation coefficient (r) and root mean square error (RMSE) for T2 (°C), PSFC (hPa), SWRAD (W m7”) and LWRAD (W m7”) obtained from each analysis/forecast products to in-situ observations.
Figure 4. Verification of top layer (0-10 cm) soil moisture (m m~°*) and soil temperature (°C) from various experiments of LDAS and GLDAS with in-situ observations (shown in Fig. 1) during monsoon season of 2011-2013. Top row represents scatter diagram of soil moisture from (a) Ex1, (b) Ex2, (c) Ex3, (d) Ex4, (e) Ex5, and (f) GLDAS against with in-situ observations. The second row (g-l) are same as (a-f) but for soil temperature. (m) represents mean statistics including standard deviation, root mean square error, and correlation coefficient of Exl, Ex2, Ex3, Ex4, Ex5, GLDAS (GLD) through Taylor diagram for soil moisture (m m~*) and (n) is same as (m) but for soil temperature. The figure is generated using MATLAB 2015a, www.imathworks.com.
Figure 4. Verification of top layer (0-10 cm) soil moisture (m m~°*) and soil temperature (°C) from various experiments of LDAS and GLDAS with in-situ observations (shown in Fig. 1) during monsoon season of 2011-2013. Top row represents scatter diagram of soil moisture from (a) Ex1, (b) Ex2, (c) Ex3, (d) Ex4, (e) Ex5, and (f) GLDAS against with in-situ observations. The second row (g-l) are same as (a-f) but for soil temperature. (m) represents mean statistics including standard deviation, root mean square error, and correlation coefficient of Exl, Ex2, Ex3, Ex4, Ex5, GLDAS (GLD) through Taylor diagram for soil moisture (m m~*) and (n) is same as (m) but for soil temperature. The figure is generated using MATLAB 2015a, www.imathworks.com.
Table 4. Parameter values for linear regression fits (y = ax + b) for ST (°C), and SM (m m> obtained from Ex1, Ex2, Ex3, Ex4, Ex5, and GLDAS products to in-situ observations.
Table 4. Parameter values for linear regression fits (y = ax + b) for ST (°C), and SM (m m> obtained from Ex1, Ex2, Ex3, Ex4, Ex5, and GLDAS products to in-situ observations.
Figure 6. Spatial distribution of soil moisture (m* m7 *) and soil temperature (°C) climatology and their anomalies in contrasting monsoon years. The JJAS climatology from (a) satellite (ESACCI), (b) GLDAS and (c) LDAS are presented. (d-f), (g-i) and (j-I) are same as (a-c) but for soil moisture anomaly for the year 2007 (an above normal monsoon year), 2009 (a deficit year) and 2010 (normal year) respectively. The JJAS soil temperature climatology from (m) GLDAS and (n) LDAS are presented. (o-p), (q-r) and (s-t) are same as (m-n), but for soil temperature anomaly for the year 2007, 2009 and 2010. The figures are generated using open source software GrADS (Grid Analysis and Display System) version 2.1.0 (http://cola.gmu.edu/ grads/ downloads.php).
Figure 6. Spatial distribution of soil moisture (m* m7 *) and soil temperature (°C) climatology and their anomalies in contrasting monsoon years. The JJAS climatology from (a) satellite (ESACCI), (b) GLDAS and (c) LDAS are presented. (d-f), (g-i) and (j-I) are same as (a-c) but for soil moisture anomaly for the year 2007 (an above normal monsoon year), 2009 (a deficit year) and 2010 (normal year) respectively. The JJAS soil temperature climatology from (m) GLDAS and (n) LDAS are presented. (o-p), (q-r) and (s-t) are same as (m-n), but for soil temperature anomaly for the year 2007, 2009 and 2010. The figures are generated using open source software GrADS (Grid Analysis and Display System) version 2.1.0 (http://cola.gmu.edu/ grads/ downloads.php).
rainfall simulation of severe convection over New Delhi, India. Verification of hourly accumulated rainfall
rainfall simulation of severe convection over New Delhi, India. Verification of hourly accumulated rainfall
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