[1] Tropical convective anvil clouds detrain preferentially near 200 hPa. It is argued here that this occurs because clear-sky radiative cooling decreases rapidly near 200 hPa. This rapid decline of clear-sky longwave cooling occurs because radiative emission from water vapor becomes inefficient above 200 hPa. The emission from water vapor becomes less important than the emission from CO 2 because the saturation vapor pressure is so very low at the temperatures above 200 hPa. This suggests that the temperature at the detrainment level, and consequently the emission temperature of tropical anvil clouds, will remain constant during climate change. This constraint has very important implications for the potential role of tropical convective clouds in climate feedback, since it means that the emission temperatures of tropical anvil clouds and upper tropospheric water vapor are essentially independent of the surface temperature, so long as the tropopause is colder than the temperature where emission from water vapor becomes relatively small.
With the global proliferation of wind power, accurate short-term forecasts of wind resources at wind energy sites are becoming paramount. Regime-switching space-time (RST) models merge meteorological and statistical expertise to obtain accurate and calibrated, fully probabilistic forecasts of wind speed and wind power. The model formulation is parsimonious, yet takes account of all the salient features of wind speed: alternating atmospheric regimes, temporal and spatial correlation, diurnal and seasonal non-stationarity, conditional heteroscedasticity, and non-Gaussianity. The RST method identifies forecast regimes at the wind energy site and fits a conditional predictive model for each regime. Geographically dispersed meteorological observations in the vicinity of the wind farm are used as off-site predictors.The RST technique was applied to 2-hour ahead forecasts of hourly average wind speed at the Stateline wind farm in the US Pacific Northwest. In July 2003, for instance, the RST forecasts had root-mean-square error (RMSE) 28.6% less than the persistence forecasts. For each month in the test period, the RST forecasts had lower RMSE than forecasts using state-of-the-art vector time series techniques. The RST method provides probabilistic forecasts in the form of predictive cumulative distribution functions, and those were well calibrated and sharp. The RST prediction intervals were substantially shorter on average than prediction intervals derived from univariate time series techniques. These results suggest that quality meteorological data from sites upwind of wind farms can be efficiently used to improve short-term forecasts of wind resources. It is anticipated that the RST technique can be successfully applied at wind energy sites all over the world. Report Documentation PageForm Public reporting burden for the collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing this burden, to Washington Headquarters Services, Directorate for Information Operations and Reports, 1215 Jefferson Davis Highway, Suite 1204, Arlington VA 22202-4302. Respondents should be aware that notwithstanding any other provision of law, no person shall be subject to a penalty for failing to comply with a collection of information if it does not display a currently valid OMB control number.
Most intensive field studies investigating aerosols have been conducted in summer, and thus, wintertime aerosol sources and chemistry are comparatively poorly understood. An aerosol mass spectrometer was flown on the National Science Foundation/National Center for Atmospheric Research C‐130 during the Wintertime INvestigation of Transport, Emissions, and Reactivity (WINTER) 2015 campaign in the northeast United States. The fraction of boundary layer submicron aerosol that was organic aerosol (OA) was about a factor of 2 smaller than during a 2011 summertime study in a similar region. However, the OA measured in WINTER was almost as oxidized as OA measured in several other studies in warmer months of the year. Fifty‐eight percent of the OA was oxygenated (secondary), and 42% was primary (POA). Biomass burning OA (likely from residential heating) was ubiquitous and accounted for 33% of the OA mass. Using nonvolatile POA, one of two default secondary OA (SOA) formulations in GEOS‐Chem (v10‐01) shows very large underpredictions of SOA and O/C (5×) and overprediction of POA (2×). We strongly recommend against using that formulation in future studies. Semivolatile POA, an alternative default in GEOS‐Chem, or a simplified parameterization (SIMPLE) were closer to the observations, although still with substantial differences. A case study of urban outflow from metropolitan New York City showed a consistent amount and normalized rate of added OA mass (due to SOA formation) compared to summer studies, although proceeding more slowly due to lower OH concentrations. A box model and SIMPLE perform similarly for WINTER as for Los Angeles, with an underprediction at ages <6 hr, suggesting that fast chemistry might be missing from the models.
The physical mechanisms that affect the tropical sea surface temperature (SST) are investigated using a twobox equilibrium model of the Tropics. One box represents the convecting, warm SST, high humidity region of the Tropics, and the other box represents the subsidence region with low humidity, boundary layer clouds, and cooler SST. The two regions communicate by energy and moisture fluxes that are proportional to the strength of the overturning circulation that couples the two regions. The boundary layer properties in the subsiding region are predicted with a mixing line model. Humidity above the inversion in the subsiding region is predicted from moisture conservation. The humidity above the inversion in the subsiding region increases rapidly with temperature, but this has less effect on the sensitivity than expected, because the inversion lowers as the humidity above the inversion is increased. Some of the increased greenhouse effect of the free troposphere can be offset by decreased greenhouse effect of the boundary layer. Increasing the area of the warm, convective region increases the SSTs, because of the greenhouse effect of the greater upper-tropospheric water vapor in the convective region. The circulation strength is constrained by radiative cooling in the cold pool. The strength of the circulation decreases with increasing convective area, because the increase in dry static stability overwhelms the increase in cooling rate. Although they have strong individual effects on longwave and shortwave radiation, high clouds in the convective region do not affect the tropical SSTs strongly, because their net radiative forcing at the top of the atmosphere is small. Low clouds in the subsidence region have a strong cooling affect on the tropical SST, because they strongly reduce net radiative heating at the top of the atmosphere. A negative feedback is produced if the low clouds are predicted from the observed relationship between stratus cloud amount and lower-tropospheric stability.
The responses of tropical clouds and water vapor to SST variations are investigated with simple numerical experiments. The fifth-generation Pennsylvania State University–National Center for Atmospheric Research (PSU–NCAR) Mesoscale Model is used with doubly periodic boundary conditions and a uniform constant sea surface temperature (SST). The SST is varied and the equilibrium statistics of cloud properties, water vapor, and circulation at different temperatures are compared. The top of the atmosphere (TOA) radiative fluxes have the same sensitivities to SST as in observations averaged from 20°N to 20°S over the Pacific, suggesting that the model sensitivities are realistic. As the SST increases, the temperature profile approximately follows a moist-adiabatic lapse rate. The rain rate and cloud ice amounts increase with SST. The average relative humidity profile stays approximately constant, but the upper-tropospheric relative humidity increases slightly with SST. The clear-sky mean temperature and water vapor feedbacks have similar magnitudes to each other and opposite signs. The net clear-sky feedback is thus about equal to the lapse rate feedback, which is about −2 W m−2 K−1. The clear-sky outgoing longwave radiation (OLR) thus increases with SST, but the high cloud-top temperature is almost constant with SST, and the high cloud amount increases with SST. The result of these three effects is an increase of cloud longwave forcing with SST and a mean OLR that is almost independent of SST. The high cloud albedo remains almost constant with increasing SST, but the increase in high cloud area causes a negative shortwave cloud radiative forcing feedback, which partly cancels the longwave cloud feedback. The net radiation decreases slightly with SST, giving a small net negative feedback, implying a stable, but very sensitive climate.
As penetrations of renewable wind energy increase, accurate short-term predictions of wind power become crucial to utilities that must balance the load and supply of electricity. As storage of wind energy is not yet feasible on a large scale, the utility must integrate wind energy as soon as it is generated and decide at each balancing time-step whether a change in conventional energy output is required. With high penetrations of wind energy, utilities must also plan for operating reserves to maintain stability of the electricity system when forecasts for renewable energy are inaccurate. Thus, a simple forecast of whether the wind power will increase, decrease or not change in the next time-step will give utility operators an easy tool for assessing whether changes need to be made to the current generation mix. In this work, Markov chain models based on the change in power output at up to three locations or lags in time are presented that not only produce such an hourly forecast but also include a measure of the uncertainty of the forecast. Forecasts are greatly improved when knowledge of whether the maximum or minimum wind power is currently being produced and the intrahour trend in wind power are incorporated. These models are trained, tested and evaluated with a uniquely long set of 2 years of 10 min measurements at four meteorological stations in the Pacific Northwest and perform better than a benchmark state-of-the-art wind speed forecasting model.
Accurate wind energy forecasts can be an essential component for economic viability of a wind project. Timely and accurate short‐term (hours) forecasts can increase the electric grid efficiency and minimize ancillary or other firming requirements, ultimately resulting in reduced costs. This article investigates the use of off‐site observations, at distances up to 200 km from the wind farm, as predictors in statistical forecast techniques. In combination with on‐site and off‐site observations, fine‐scale numerical weather predictions can also be used to further increase forecast accuracy at these short forecast horizons. An example from the Pacific Northwest of the USA is described. It is shown that significant forecast improvements are feasible when using off‐site observations and/or mesoscale numerical weather predictions in statistical forecast algorithms. Copyright © 2005 John Wiley & Sons, Ltd.
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