Search Results
You are looking at 1 - 5 of 5 items for
- Author or Editor: Z.-L. Yang x
- Refine by Access: All Content x
The Project for Intercomparison of Land-surface Parameterization Schemes (PILPS) is described and the first stage science plan outlined. PILPS is a project designed to improve the parameterization of the continental surface, especially the hydrological, energy, momentum, and carbon exchanges with the atmosphere. The PILPS Science Plan incorporates enhanced documentation, comparison, and validation of continental surface parameterization schemes by community participation. Potential participants include code developers, code users, and those who can provide datasets for validation and who have expertise of value in this exercise. PILPS is an important activity because existing intercomparisons, although piecemeal, demonstrate that there are significant differences in the formulation of individual processes in the available land surface schemes. These differences are comparable to other recognized differences among current global climate models such as cloud and convection parameterizations. It is also clear that too few sensitivity studies have been undertaken with the result that there is not yet enough information to indicate which simplifications or omissions are important for the near-surface continental climate, hydrology, and biogeochemistry. PILPS emphasizes sensitivity studies with and intercomparisons of existing land surface codes and the development of areally extensive datasets for their testing and validation.
The Project for Intercomparison of Land-surface Parameterization Schemes (PILPS) is described and the first stage science plan outlined. PILPS is a project designed to improve the parameterization of the continental surface, especially the hydrological, energy, momentum, and carbon exchanges with the atmosphere. The PILPS Science Plan incorporates enhanced documentation, comparison, and validation of continental surface parameterization schemes by community participation. Potential participants include code developers, code users, and those who can provide datasets for validation and who have expertise of value in this exercise. PILPS is an important activity because existing intercomparisons, although piecemeal, demonstrate that there are significant differences in the formulation of individual processes in the available land surface schemes. These differences are comparable to other recognized differences among current global climate models such as cloud and convection parameterizations. It is also clear that too few sensitivity studies have been undertaken with the result that there is not yet enough information to indicate which simplifications or omissions are important for the near-surface continental climate, hydrology, and biogeochemistry. PILPS emphasizes sensitivity studies with and intercomparisons of existing land surface codes and the development of areally extensive datasets for their testing and validation.
Abstract
The energy and water balances at the earth's surface are dramatically influenced by the presence of snow cover. Therefore, soil temperature and moisture for snow-covered and snow-free areas can be very different. In computing these soil state variables, many land surface schemes in climate models do not explicitly distinguish between snow-covered and snow-free areas. Even if they do, some schemes average these state variables to calculate grid-mean energy fluxes and these averaged state variables are then used at the beginning of the next time step. This latter approach introduces a numerical error in that heat is redistributed from snow-free areas to snow-covered areas, resulting in a more rapid snowmelt. This study focuses on the latter approach and examines the sensitivity of soil moisture and streamflow to the treatment of the soil state variables in the presence of snow cover by using WATCLASS, a land surface scheme linked with a hydrologic model. The model was tested for the 1993 snowmelt period on the Upper Grand River in Southern Ontario, Canada. The results show that a more realistic simulation of streamflow can be obtained by keeping track of the soil states in snow-covered and snow-free areas.
Abstract
The energy and water balances at the earth's surface are dramatically influenced by the presence of snow cover. Therefore, soil temperature and moisture for snow-covered and snow-free areas can be very different. In computing these soil state variables, many land surface schemes in climate models do not explicitly distinguish between snow-covered and snow-free areas. Even if they do, some schemes average these state variables to calculate grid-mean energy fluxes and these averaged state variables are then used at the beginning of the next time step. This latter approach introduces a numerical error in that heat is redistributed from snow-free areas to snow-covered areas, resulting in a more rapid snowmelt. This study focuses on the latter approach and examines the sensitivity of soil moisture and streamflow to the treatment of the soil state variables in the presence of snow cover by using WATCLASS, a land surface scheme linked with a hydrologic model. The model was tested for the 1993 snowmelt period on the Upper Grand River in Southern Ontario, Canada. The results show that a more realistic simulation of streamflow can be obtained by keeping track of the soil states in snow-covered and snow-free areas.
Abstract
A comparative study of three snow models with different complexities was carried out to assess how a physically detailed snow model can improve snow modeling within general circulation models. The three models were (a) the U.S. Army Cold Regions Research and Engineering Laboratory Model (SNTHERM), which uses the mixture theory to simulate multiphase water and energy transfer processes in snow layers; (b) a simplified three-layer model, Snow–Atmosphere–Soil Transfer (SAST), which includes only the ice and liquid-water phases;and (c) the snow submodel of the Biosphere–Atmosphere Transfer Scheme (BATS), which calculates snowmelt from the energy budget and snow temperature by the force–restore method. Given the same initial conditions and forcing of atmosphere and radiation, these three models simulated time series of snow water equivalent, surface temperature, and fluxes very well, with SNTHERM giving the best match with observations and SAST simulation being close. BATS captured the major processes in the upper portion of a snowpack where solar radiation provides the main energy source and gave satisfying results for seasonal periods. Some biases occurred in BATS surface temperature and energy exchange due to its neglecting of liquid water and underestimating snow density. Ice heat conduction, meltwater heat transport, and the melt–freeze process of snow exhibit strong diurnal variations and large gradients at the uppermost layers of snowpacks. Using two layers in the upper 20 cm and one deeper layer at the bottom to simulate the multiphase snowmelt processes, SAST closely approximated the performance of SNTHERM with computational requirements comparable to those of BATS.
Abstract
A comparative study of three snow models with different complexities was carried out to assess how a physically detailed snow model can improve snow modeling within general circulation models. The three models were (a) the U.S. Army Cold Regions Research and Engineering Laboratory Model (SNTHERM), which uses the mixture theory to simulate multiphase water and energy transfer processes in snow layers; (b) a simplified three-layer model, Snow–Atmosphere–Soil Transfer (SAST), which includes only the ice and liquid-water phases;and (c) the snow submodel of the Biosphere–Atmosphere Transfer Scheme (BATS), which calculates snowmelt from the energy budget and snow temperature by the force–restore method. Given the same initial conditions and forcing of atmosphere and radiation, these three models simulated time series of snow water equivalent, surface temperature, and fluxes very well, with SNTHERM giving the best match with observations and SAST simulation being close. BATS captured the major processes in the upper portion of a snowpack where solar radiation provides the main energy source and gave satisfying results for seasonal periods. Some biases occurred in BATS surface temperature and energy exchange due to its neglecting of liquid water and underestimating snow density. Ice heat conduction, meltwater heat transport, and the melt–freeze process of snow exhibit strong diurnal variations and large gradients at the uppermost layers of snowpacks. Using two layers in the upper 20 cm and one deeper layer at the bottom to simulate the multiphase snowmelt processes, SAST closely approximated the performance of SNTHERM with computational requirements comparable to those of BATS.
Abstract
From 2014 to 2017, two Department of Energy buoys equipped with Doppler lidar were deployed off the U.S. East Coast to provide long-term measurements of hub-height wind speed in the marine environment. We performed simulations of selected cases from the deployment using a 5-km configuration of the Weather Research and Forecasting (WRF) Model, to see if simulated hub-height speeds could produce closer agreement with the observations than existing reanalysis products. For each case we performed two additional simulations: one in which marine surface roughness height was one-way coupled to forecast wave parameters from a stand-alone WaveWatch III (WW3) simulation, and another in which WRF and WW3 were two-way coupled using the Coupled Ocean–Atmosphere–Wave–Sediment–Transport (COAWST) framework. It was found that all the 5-km WRF simulations improved 90-m wind speed statistics for the tropical cyclone case of 8 May 2015 and the cold frontal case of 25 March 2016, but not the nor’easter of 18 January 2016. The impact of wave coupling on buoy-level (4 m) wind speed was modest and case dependent, but when present, the impact was typically seen at 90 m as well, being as large as 10% in stable conditions. One-way wave coupling consistently reduced wind speeds, improving biases for 25 March 2016 but worsening them for 8 May 2015. Two-way wave coupling mitigated these negative biases, improved wave field representation and statistics, and mostly improved 4-m wind field correlation coefficients, at least at the Virginia buoy, largely due to greater self-consistency between wind and wave fields.
Significance Statement
Using atmospheric models to forecast winds in the environments of offshore wind turbines will be critical in the new energy economy. The models used are imperfect, however, being sometimes too coarse, and may not properly represent the wind field at typical turbine hub heights of 90 m, for which we have limited observations in the marine environment. To help address this gap, two buoys equipped with lidars that measured hub-height winds continuously were deployed off the U.S. East Coast from 2014 to 2017. We used the lidar buoy data to show the benefits of a relatively high-resolution atmospheric model over existing reanalysis products, as well as including both the impacts of waves on winds and vice versa.
Abstract
From 2014 to 2017, two Department of Energy buoys equipped with Doppler lidar were deployed off the U.S. East Coast to provide long-term measurements of hub-height wind speed in the marine environment. We performed simulations of selected cases from the deployment using a 5-km configuration of the Weather Research and Forecasting (WRF) Model, to see if simulated hub-height speeds could produce closer agreement with the observations than existing reanalysis products. For each case we performed two additional simulations: one in which marine surface roughness height was one-way coupled to forecast wave parameters from a stand-alone WaveWatch III (WW3) simulation, and another in which WRF and WW3 were two-way coupled using the Coupled Ocean–Atmosphere–Wave–Sediment–Transport (COAWST) framework. It was found that all the 5-km WRF simulations improved 90-m wind speed statistics for the tropical cyclone case of 8 May 2015 and the cold frontal case of 25 March 2016, but not the nor’easter of 18 January 2016. The impact of wave coupling on buoy-level (4 m) wind speed was modest and case dependent, but when present, the impact was typically seen at 90 m as well, being as large as 10% in stable conditions. One-way wave coupling consistently reduced wind speeds, improving biases for 25 March 2016 but worsening them for 8 May 2015. Two-way wave coupling mitigated these negative biases, improved wave field representation and statistics, and mostly improved 4-m wind field correlation coefficients, at least at the Virginia buoy, largely due to greater self-consistency between wind and wave fields.
Significance Statement
Using atmospheric models to forecast winds in the environments of offshore wind turbines will be critical in the new energy economy. The models used are imperfect, however, being sometimes too coarse, and may not properly represent the wind field at typical turbine hub heights of 90 m, for which we have limited observations in the marine environment. To help address this gap, two buoys equipped with lidars that measured hub-height winds continuously were deployed off the U.S. East Coast from 2014 to 2017. We used the lidar buoy data to show the benefits of a relatively high-resolution atmospheric model over existing reanalysis products, as well as including both the impacts of waves on winds and vice versa.
Before and during the 2008 Beijing Olympics from June to September, ground-based and satellite monitoring were carried out over Beijing and its vicinity (BIV) in a campaign to quantify the outcomes of various emission control measures. These include hourly surface PM10 and PM2.5 and their fraction of black carbon (BC), organics, nitrate, sulfate, ammonium, and daily aerosol optical depth (AOD), together with hourly reactive gases, surface ozone, and daily columnar NO2 from satellite. The analyses, excluding the estimates from weather contributions, demonstrate that after the control measures, including banning ~300,000 “yellow-tag” vehicles from roads, the even–odd turn of motor vehicles on the roads, and emission reduction aiming at coal combustion, were implemented, air quality in Beijing improved substantially. The levels of NO, NO2, NOx, CO, SO2, BC, organics, and nitrate dropped by about 30%–60% and the ozone moderately increased by ~40% while the sulfate and ammonium exhibited different patterns during various control stages. Weather conditions have a great impact on the summertime secondary aerosol (~80% of total PM) and O3 formations over BIV. During the Olympic Game period, various atmospheric components decreased dramatically at Beijing compared to the same period in the previous years. This decrease was related not only to the implementation of rigorous control measures, but also to the favorable weather processes. The subtropical high was located to the south so that Beijing's weather was dominated by the interaction between a frequently eastward shifting trough in the westerlies and a cold continental high with clear to cloudy days or showery weather.
Before and during the 2008 Beijing Olympics from June to September, ground-based and satellite monitoring were carried out over Beijing and its vicinity (BIV) in a campaign to quantify the outcomes of various emission control measures. These include hourly surface PM10 and PM2.5 and their fraction of black carbon (BC), organics, nitrate, sulfate, ammonium, and daily aerosol optical depth (AOD), together with hourly reactive gases, surface ozone, and daily columnar NO2 from satellite. The analyses, excluding the estimates from weather contributions, demonstrate that after the control measures, including banning ~300,000 “yellow-tag” vehicles from roads, the even–odd turn of motor vehicles on the roads, and emission reduction aiming at coal combustion, were implemented, air quality in Beijing improved substantially. The levels of NO, NO2, NOx, CO, SO2, BC, organics, and nitrate dropped by about 30%–60% and the ozone moderately increased by ~40% while the sulfate and ammonium exhibited different patterns during various control stages. Weather conditions have a great impact on the summertime secondary aerosol (~80% of total PM) and O3 formations over BIV. During the Olympic Game period, various atmospheric components decreased dramatically at Beijing compared to the same period in the previous years. This decrease was related not only to the implementation of rigorous control measures, but also to the favorable weather processes. The subtropical high was located to the south so that Beijing's weather was dominated by the interaction between a frequently eastward shifting trough in the westerlies and a cold continental high with clear to cloudy days or showery weather.
