Search Results
is calculated so that the average SoP ratio of V 2 during 2010–21 ( SoP V 2 ) equals the one from the SoP regressed map (SoP RM ). The parameter f can be solved for algebraically as follows ( S : snow; M : mixed, R : rain): SoP V 2 = SoP RM , ∑ S V 2 ∑ S V 2 + ∑ M V 2 + ∑ R V 2 = SoP RM . Using the relations from steps 4 and 5 above, ∑ S V 0 Alt × ( 1 − f ) + ∑ S V 1 × f ∑ S V 0 Alt × ( 1 − f ) + ∑ S V 1 × f + ∑ M V 1 + ∑ R V 1 = SoP RM . And solving for f we obtain f = SoP RM × ( ∑ S V 0
is calculated so that the average SoP ratio of V 2 during 2010–21 ( SoP V 2 ) equals the one from the SoP regressed map (SoP RM ). The parameter f can be solved for algebraically as follows ( S : snow; M : mixed, R : rain): SoP V 2 = SoP RM , ∑ S V 2 ∑ S V 2 + ∑ M V 2 + ∑ R V 2 = SoP RM . Using the relations from steps 4 and 5 above, ∑ S V 0 Alt × ( 1 − f ) + ∑ S V 1 × f ∑ S V 0 Alt × ( 1 − f ) + ∑ S V 1 × f + ∑ M V 1 + ∑ R V 1 = SoP RM . And solving for f we obtain f = SoP RM × ( ∑ S V 0
-resolution surface water maps as well as water height for rivers, lakes, inundated areas, and wetlands. Before the launch of this mission and in the framework of its preparation, long-term datasets of high-spatial-resolution surface water extent are in demand. Would it be possible to develop downscaling methodology to derive high-resolution surface water extent from the existing GIEMS low-resolution dataset? Since GIEMS has a global coverage, the ideal situation would be to develop a downscaling technique
-resolution surface water maps as well as water height for rivers, lakes, inundated areas, and wetlands. Before the launch of this mission and in the framework of its preparation, long-term datasets of high-spatial-resolution surface water extent are in demand. Would it be possible to develop downscaling methodology to derive high-resolution surface water extent from the existing GIEMS low-resolution dataset? Since GIEMS has a global coverage, the ideal situation would be to develop a downscaling technique
role in the computation of the principal mass and energy fluxes. In the literature these parameters are usually defined using soil texture maps ( Rawls and Brakensiek 1985 ), but problems of representativeness arise owing to pixel heterogeneity. Satellite images of land surface temperature can help in the calibration of these parameters in each pixel of the analyzed domain, overcoming the traditional calibration based on a single multiplicative value retrieved from the comparison between observed
role in the computation of the principal mass and energy fluxes. In the literature these parameters are usually defined using soil texture maps ( Rawls and Brakensiek 1985 ), but problems of representativeness arise owing to pixel heterogeneity. Satellite images of land surface temperature can help in the calibration of these parameters in each pixel of the analyzed domain, overcoming the traditional calibration based on a single multiplicative value retrieved from the comparison between observed
) were used as the second criterion in the proposed methodology. Fig . 4. Data sources used to delimit the aquifer basin boundaries: (a) groundwater resources of the world according to the WHYMAP (BGR and UNESCO; http://www.whymap.org ), (b) the BGR geological units by age (BGR; http://www.bgr.de/karten/igme5000/igme5000.htm ), and (c) the simplified lithology of France (BRGM; http://infoterre.brgm.fr ). The comparison of the BDRHF map ( Fig. 3 ) with the two selected classes of WHYMAP, the IGME
) were used as the second criterion in the proposed methodology. Fig . 4. Data sources used to delimit the aquifer basin boundaries: (a) groundwater resources of the world according to the WHYMAP (BGR and UNESCO; http://www.whymap.org ), (b) the BGR geological units by age (BGR; http://www.bgr.de/karten/igme5000/igme5000.htm ), and (c) the simplified lithology of France (BRGM; http://infoterre.brgm.fr ). The comparison of the BDRHF map ( Fig. 3 ) with the two selected classes of WHYMAP, the IGME
rainfall fields down to drop scale using data collected by a 2D video disdrometer (2DVD) [see Kruger and Krajewski (2002) for a precise description of the device’s functioning], deployed in the Ardèche region (southeastern France) in the framework of the Hydrological Cycle in Mediterranean Experiment (HyMeX; Ducrocq et al. 2014 ), in an innovative way. Indeed, this device has been extensively used as a reference in comparison with other rainfall-measuring ones ( Krajewski et al. 2006 ; Tokay et al
rainfall fields down to drop scale using data collected by a 2D video disdrometer (2DVD) [see Kruger and Krajewski (2002) for a precise description of the device’s functioning], deployed in the Ardèche region (southeastern France) in the framework of the Hydrological Cycle in Mediterranean Experiment (HyMeX; Ducrocq et al. 2014 ), in an innovative way. Indeed, this device has been extensively used as a reference in comparison with other rainfall-measuring ones ( Krajewski et al. 2006 ; Tokay et al
are also compared with CMORPH, TMPA, a merged microwave product used in the creation of CMORPH called microwave combination (MWCOMB), and the GPCP 1DD daily data. Among these merged precipitation products used for comparison, TMPA is the only product that applies gauge corrections. Table 4 shows bias, correlation, and RMSD of the CHOMPS, CMORPH, and TMPA evaluated against the stage IV data ( Lin and Mitchell 2005 ). Figure 5 shows maps of the correlations and biases. This analysis is similar to
are also compared with CMORPH, TMPA, a merged microwave product used in the creation of CMORPH called microwave combination (MWCOMB), and the GPCP 1DD daily data. Among these merged precipitation products used for comparison, TMPA is the only product that applies gauge corrections. Table 4 shows bias, correlation, and RMSD of the CHOMPS, CMORPH, and TMPA evaluated against the stage IV data ( Lin and Mitchell 2005 ). Figure 5 shows maps of the correlations and biases. This analysis is similar to
′ and y ′, and r ′ x′y′ represents the spatial anomaly correlation coefficient. Equation (15) can be manipulated algebraically by dividing by S 2 y ′ and completing the square to give the normalized MSE (NMSE), represented as where A 2 is a nondimensional measure of the unconditional bias, B 2 is a measure of the conditional bias, C 2 is the phase error, and NMSE is the normalized MSE, where NMSE = MSE/ S 2 y ′ . For analyses of fields (maps), the terms in (16) can be physically
′ and y ′, and r ′ x′y′ represents the spatial anomaly correlation coefficient. Equation (15) can be manipulated algebraically by dividing by S 2 y ′ and completing the square to give the normalized MSE (NMSE), represented as where A 2 is a nondimensional measure of the unconditional bias, B 2 is a measure of the conditional bias, C 2 is the phase error, and NMSE is the normalized MSE, where NMSE = MSE/ S 2 y ′ . For analyses of fields (maps), the terms in (16) can be physically
z = d 1 . The difference θ g − θ g eq must represent the effect of the vertical gradient of hydraulic potential (between surface zone and lower root-zone region), with C 2 serving to map moisture content to matric potential units and to scale by the conductivity. The most significant obstacle for the stratified soil case is the θ g eq term. Recall from (3)–(5) that θ g eq is a function of θ 2 , with adjustments to account for the relative importance of capillary and gravity forces
z = d 1 . The difference θ g − θ g eq must represent the effect of the vertical gradient of hydraulic potential (between surface zone and lower root-zone region), with C 2 serving to map moisture content to matric potential units and to scale by the conductivity. The most significant obstacle for the stratified soil case is the θ g eq term. Recall from (3)–(5) that θ g eq is a function of θ 2 , with adjustments to account for the relative importance of capillary and gravity forces
the assimilation of soil moisture by matching the two cumulative distribution functions (CDFs) over a comparison period. One assumption of CDF matching is that the ranked values of the two datasets are uniquely related. In practice this usually does not hold due to estimation uncertainties in both the retrieved and observed/modeled soil moisture, resulting in a statistical rather than a perfect dependency. An alternative approach is to construct statistical observation operators by fitting a
the assimilation of soil moisture by matching the two cumulative distribution functions (CDFs) over a comparison period. One assumption of CDF matching is that the ranked values of the two datasets are uniquely related. In practice this usually does not hold due to estimation uncertainties in both the retrieved and observed/modeled soil moisture, resulting in a statistical rather than a perfect dependency. An alternative approach is to construct statistical observation operators by fitting a
Beijing municipality, some 40 km south-southwest of the city center ( Fig. 1 ). Beijing has a typical continental monsoon climate, with four distinct seasons; cool and wet winters are dominated by the polar air mass, while hot and rainy summers are influenced by the East Asian summer monsoon. The annual precipitation is 574 mm, of which some 70% occurs during the summer season of June–September. Average annual temperature is around 12°C ( Fig. 2 ). Fig . 1. Sketched map showing the sampling location
Beijing municipality, some 40 km south-southwest of the city center ( Fig. 1 ). Beijing has a typical continental monsoon climate, with four distinct seasons; cool and wet winters are dominated by the polar air mass, while hot and rainy summers are influenced by the East Asian summer monsoon. The annual precipitation is 574 mm, of which some 70% occurs during the summer season of June–September. Average annual temperature is around 12°C ( Fig. 2 ). Fig . 1. Sketched map showing the sampling location
