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occurred within a month of the same time of year, because you wouldn't expect that fall maps and midwinter maps would be much alike anyway, and hoping maybe I could find a few cases where the difference between them or some measure (say rms difference) between the two fields was only half of that between two randomly chosen fields. But the best that I found of these few hundred thousand comparisons was one case I think where it was 62%. It didn't seem like a very good analog somehow but it was enough
occurred within a month of the same time of year, because you wouldn't expect that fall maps and midwinter maps would be much alike anyway, and hoping maybe I could find a few cases where the difference between them or some measure (say rms difference) between the two fields was only half of that between two randomly chosen fields. But the best that I found of these few hundred thousand comparisons was one case I think where it was 62%. It didn't seem like a very good analog somehow but it was enough
The Amazon Dense GNSS Meteorological Network provides high spatiotemporal resolution, all-weather precipitable water vapor for studying the evolution of continental tropical and sea-breeze convective regimes of Amazonia. The meteorology and climate of the equatorial tropics are dominated by atmospheric convection, which presents a rather challenging range of spatial and temporal scales to capture with present-day observational platforms ( Mapes and Neale 2011 ; Moncrieff et al. 2012 ; Zhang
The Amazon Dense GNSS Meteorological Network provides high spatiotemporal resolution, all-weather precipitable water vapor for studying the evolution of continental tropical and sea-breeze convective regimes of Amazonia. The meteorology and climate of the equatorial tropics are dominated by atmospheric convection, which presents a rather challenging range of spatial and temporal scales to capture with present-day observational platforms ( Mapes and Neale 2011 ; Moncrieff et al. 2012 ; Zhang
. An example of a model–measurement comparison is shown in Fig. SB3 , in which an OSIRIS spectrum is compared with a simulation from the SaskTRAN model ( Bourassa et al. 2007b ). This example illustrates how models also provide useful diagnostic information that aid in the interpretation of the observed spectra. Here the contribution to the total signal from the single-scattered, multiple-scattered, and surface-reflected components are shown. Fig. SB3. Comparison of OSIRIS- and SaskTRAN
. An example of a model–measurement comparison is shown in Fig. SB3 , in which an OSIRIS spectrum is compared with a simulation from the SaskTRAN model ( Bourassa et al. 2007b ). This example illustrates how models also provide useful diagnostic information that aid in the interpretation of the observed spectra. Here the contribution to the total signal from the single-scattered, multiple-scattered, and surface-reflected components are shown. Fig. SB3. Comparison of OSIRIS- and SaskTRAN
), conceptual insights from energy are not common. Four broad weather-focused introductory texts— Meteorology Today: An Introduction to Weather, Climate, and the Environment ( Ahrens 2021 ), Practical Meteorology: An Algebra-Based Survey of Atmospheric Science ( Stull 2015 ), The Atmosphere: An Introduction to Meteorology ( Lutgens and Tarbuck 2015 ), and Weather: A Concise Introduction ( Hakim and Patoux 2021 )—do not mention static energy, and they explain the dry adiabatic lapse rate solely in
), conceptual insights from energy are not common. Four broad weather-focused introductory texts— Meteorology Today: An Introduction to Weather, Climate, and the Environment ( Ahrens 2021 ), Practical Meteorology: An Algebra-Based Survey of Atmospheric Science ( Stull 2015 ), The Atmosphere: An Introduction to Meteorology ( Lutgens and Tarbuck 2015 ), and Weather: A Concise Introduction ( Hakim and Patoux 2021 )—do not mention static energy, and they explain the dry adiabatic lapse rate solely in
backscatter profiles from over 265 ALCs in 19 countries are being distributed by EUMETNET E-PROFILE in near–real time to national weather services and can be viewed online (at http://eumetnet.eu/e-profile/ ). These data are homogenized and calibrated using the developments carried out in TOPROF. Figure 1 represents the map of E-PROFILE stations in green and stations that will be integrated before the end of 2018 in blue: ALCs that are present in Europe but not yet integrated into E-PROFILE are in red
backscatter profiles from over 265 ALCs in 19 countries are being distributed by EUMETNET E-PROFILE in near–real time to national weather services and can be viewed online (at http://eumetnet.eu/e-profile/ ). These data are homogenized and calibrated using the developments carried out in TOPROF. Figure 1 represents the map of E-PROFILE stations in green and stations that will be integrated before the end of 2018 in blue: ALCs that are present in Europe but not yet integrated into E-PROFILE are in red
simulations spanning the whole twenty-first century ( Jacob et al. 2014 ) to identify the key features of meteorological input data with relevance for the output of power system simulations. The hackathon team developed advanced statistical comparisons of model simulations, including a very large covariance correlation matrix on many key input/output parameters. This highlighted some important similarities and marked differences between climate models. For example, a stark difference (sign flip) was
simulations spanning the whole twenty-first century ( Jacob et al. 2014 ) to identify the key features of meteorological input data with relevance for the output of power system simulations. The hackathon team developed advanced statistical comparisons of model simulations, including a very large covariance correlation matrix on many key input/output parameters. This highlighted some important similarities and marked differences between climate models. For example, a stark difference (sign flip) was
). Consistent with earlier studies, such as Simmons et al. (2004a) , the conversion map shows that internal tides are generated in areas of rough topography such as the Hawaiian Ridge. The HYCOM–mooring comparison map in Fig. 4c indicates that the HYCOM simulations are able to predict tidal fluxes with some reasonable degree of accuracy. Buijsman et al. (2016) found that about 12% of these low modes reach the continental slopes, compared to 31% found by Waterhouse et al. (2014) . The HYCOM results
). Consistent with earlier studies, such as Simmons et al. (2004a) , the conversion map shows that internal tides are generated in areas of rough topography such as the Hawaiian Ridge. The HYCOM–mooring comparison map in Fig. 4c indicates that the HYCOM simulations are able to predict tidal fluxes with some reasonable degree of accuracy. Buijsman et al. (2016) found that about 12% of these low modes reach the continental slopes, compared to 31% found by Waterhouse et al. (2014) . The HYCOM results
thunderstorms and tropical storms can also cause floods and flash floods during the summer and fall. The model is currently being used at the National Weather Service Binghamton, New York, Weather Forecast Office in an experimental fashion. Fig. 2. Site map showing the modeled upper Delaware basin watersheds. Shown are the locations of streams and stream gauges, watershed boundaries, delineated hillslopes used for KINEROS, and ~1-km grid boxes on which the snow and subsurface models run. MODEL DESCRIPTION
thunderstorms and tropical storms can also cause floods and flash floods during the summer and fall. The model is currently being used at the National Weather Service Binghamton, New York, Weather Forecast Office in an experimental fashion. Fig. 2. Site map showing the modeled upper Delaware basin watersheds. Shown are the locations of streams and stream gauges, watershed boundaries, delineated hillslopes used for KINEROS, and ~1-km grid boxes on which the snow and subsurface models run. MODEL DESCRIPTION
area of interest to glacier–climate studies ( Pratap et al. 2015 ). The Pumori Bench is also relatively stable, and therefore suitable for precipitation sensors and a double-alter wind shield. Due to its proximity to such a well-known location on the Everest climbing route, we refer to this station as “Base Camp” hereafter. F ig . 2. (center) Map of locations referred to in the text. (left),(right) Photographs of the automatic weather stations installed during the 2019 Everest Expedition
area of interest to glacier–climate studies ( Pratap et al. 2015 ). The Pumori Bench is also relatively stable, and therefore suitable for precipitation sensors and a double-alter wind shield. Due to its proximity to such a well-known location on the Everest climbing route, we refer to this station as “Base Camp” hereafter. F ig . 2. (center) Map of locations referred to in the text. (left),(right) Photographs of the automatic weather stations installed during the 2019 Everest Expedition
-Past precipitation and air temperature calculated using daily station observations (see Fig. S1 in the online supplemental material for maps with MAE values for the other variables and see appendix C for details on the station data sources and postprocessing; https://doi.org/10.1175/BAMS-D-21-0145.2 ). For precipitation, MAE values are consistently high (>5 mm day −1 ) in low-latitude regions, such as the Amazon, central Africa, and Southeast Asia, which is attributable to two factors. First, the high
-Past precipitation and air temperature calculated using daily station observations (see Fig. S1 in the online supplemental material for maps with MAE values for the other variables and see appendix C for details on the station data sources and postprocessing; https://doi.org/10.1175/BAMS-D-21-0145.2 ). For precipitation, MAE values are consistently high (>5 mm day −1 ) in low-latitude regions, such as the Amazon, central Africa, and Southeast Asia, which is attributable to two factors. First, the high
