Search Results
-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
sample globally, obtaining mean temperatures and velocities over 9–25-day intervals and therefore are averaging over several full tidal and internal wave cycles. One of the design objectives in deploying ALACE floats was to measure absolute reference velocities for use in refining hydrographic transport estimates. The first goal of this study is to make use of ALACE measurements to map mean temperature and dynamic topography in the Southern Ocean. ALACE temperatures are then compared with
sample globally, obtaining mean temperatures and velocities over 9–25-day intervals and therefore are averaging over several full tidal and internal wave cycles. One of the design objectives in deploying ALACE floats was to measure absolute reference velocities for use in refining hydrographic transport estimates. The first goal of this study is to make use of ALACE measurements to map mean temperature and dynamic topography in the Southern Ocean. ALACE temperatures are then compared with
value of a~ in SMC tends to have asimilar effect to increasing values of a in the PP scheme. While the above technique is useful for relating thesimpler parameterizations, it is of limited value whenthe TKE equation is no longer algebraic as, for example, in the level 21/2 closure models. To continuethe comparison we use data sampled from four differentruns of the Pacific Ocean model, each integrated to aseasonal equilibrium: (i) a model using PP with parameters as describedin section 2 (ii
value of a~ in SMC tends to have asimilar effect to increasing values of a in the PP scheme. While the above technique is useful for relating thesimpler parameterizations, it is of limited value whenthe TKE equation is no longer algebraic as, for example, in the level 21/2 closure models. To continuethe comparison we use data sampled from four differentruns of the Pacific Ocean model, each integrated to aseasonal equilibrium: (i) a model using PP with parameters as describedin section 2 (ii
for sake of comparison. Overall, GC performs the worst with numerical blow up at . The scalar map ME2 produces improved filter estimates over GC except on but it converges for the case of . The vector map ME2 beats these two cases on all counts. Also, the filtering skills of the vector maps ME1 and ME2 are visually indistinguishable. For some of the fields, most notably for , , , and , their skills are close to that of the perfect-model vector map experiment PM . Interestingly, in
for sake of comparison. Overall, GC performs the worst with numerical blow up at . The scalar map ME2 produces improved filter estimates over GC except on but it converges for the case of . The vector map ME2 beats these two cases on all counts. Also, the filtering skills of the vector maps ME1 and ME2 are visually indistinguishable. For some of the fields, most notably for , , , and , their skills are close to that of the perfect-model vector map experiment PM . Interestingly, in
In a recent article published in this journal, Ting et al. (1996) documented the three-dimensional structure of the stationary wave response to a distinctive mode of variability of the zonally averaged basic state: a meridional “see-saw” in zonal momentum represented by the algebraic difference between the zonal-mean, zonal (geostrophic) wind at 55° and 35°N. This particular choice of index was motivated by statistics derived from a suite of experiments with a linear baroclinic stationary
In a recent article published in this journal, Ting et al. (1996) documented the three-dimensional structure of the stationary wave response to a distinctive mode of variability of the zonally averaged basic state: a meridional “see-saw” in zonal momentum represented by the algebraic difference between the zonal-mean, zonal (geostrophic) wind at 55° and 35°N. This particular choice of index was motivated by statistics derived from a suite of experiments with a linear baroclinic stationary
tend to scale linearly with annual precipitation (i.e., wetter climates have larger rmse’s). This average is about 39% of the 30-yr monthly average precipitation for the contiguous United States. Table 1 also includes the ratio of the rmse to the standard deviation of all months in the test period for each forecast division, with the corresponding map shown in Fig. 5 . This is a comparison between method error and the variability of the test data. The unitless ratio ranges from about 0.4 to 0
tend to scale linearly with annual precipitation (i.e., wetter climates have larger rmse’s). This average is about 39% of the 30-yr monthly average precipitation for the contiguous United States. Table 1 also includes the ratio of the rmse to the standard deviation of all months in the test period for each forecast division, with the corresponding map shown in Fig. 5 . This is a comparison between method error and the variability of the test data. The unitless ratio ranges from about 0.4 to 0
observations. The first of these is found by means of accurate MCMC simulations and is then characterized by three quantities: its mean, variance, and MAP estimator. It is our contention that, where quantification of uncertainty is important, the comparison of algorithms by their ability to predict (i) is central; however many algorithms are benchmarked in the literature by their ability to predict the truth [(ii)] and so we also include this information. A comparison of the algorithms with the
observations. The first of these is found by means of accurate MCMC simulations and is then characterized by three quantities: its mean, variance, and MAP estimator. It is our contention that, where quantification of uncertainty is important, the comparison of algorithms by their ability to predict (i) is central; however many algorithms are benchmarked in the literature by their ability to predict the truth [(ii)] and so we also include this information. A comparison of the algorithms with the
with I.At least 63 (75) per cent of the time, term I is 10 (4)times larger than term 11, or more. These statisticswere based upon 236 comparisons of terms I and 11.These comparisons were made from ten consecutive1500 GCT 500-mb maps beginning with 13 February1953. From each map, these comparisons were madefrom a geographical grid of 48 points equally spacedover the United States. In these comparisons, thefields of wind and pressure were analyzed independently; their functions I and I1 in (5) were
with I.At least 63 (75) per cent of the time, term I is 10 (4)times larger than term 11, or more. These statisticswere based upon 236 comparisons of terms I and 11.These comparisons were made from ten consecutive1500 GCT 500-mb maps beginning with 13 February1953. From each map, these comparisons were madefrom a geographical grid of 48 points equally spacedover the United States. In these comparisons, thefields of wind and pressure were analyzed independently; their functions I and I1 in (5) were
map algebra functions for combining the surface interpolation maps (ratios; see procedure 3) and the respective PRISM monthly grids. The map algebra functions combine data on a cell-by-cell basis to derive the final target information grid dataset. In this way, operating on each cell, the target daily precipitation grid was obtained as the result of the following combination: where daily P ( i ) is precipitation grid at day i , daily I r ( i ) is grid of IDW interpolated station ratios [see Eq
map algebra functions for combining the surface interpolation maps (ratios; see procedure 3) and the respective PRISM monthly grids. The map algebra functions combine data on a cell-by-cell basis to derive the final target information grid dataset. In this way, operating on each cell, the target daily precipitation grid was obtained as the result of the following combination: where daily P ( i ) is precipitation grid at day i , daily I r ( i ) is grid of IDW interpolated station ratios [see Eq
, the routine 300-mb. map analysis isreasonably accurate. Table 4 gives the algebraic andabsolute values of the angle between wind and geostrophicwind (i) and the difference in speed and geostrophicspeed (V- VE) over the Pacific and over the United Statesobtained from comparisons of 6-hour average transosondevelocities and geostrophic velocities. The comparisonwas limited to the months of September, October, andNovember, since in December the transosonde positionsbegan to be plotted and utilized as
, the routine 300-mb. map analysis isreasonably accurate. Table 4 gives the algebraic andabsolute values of the angle between wind and geostrophicwind (i) and the difference in speed and geostrophicspeed (V- VE) over the Pacific and over the United Statesobtained from comparisons of 6-hour average transosondevelocities and geostrophic velocities. The comparisonwas limited to the months of September, October, andNovember, since in December the transosonde positionsbegan to be plotted and utilized as
