Weather station

About: Weather station is a research topic. Over the lifetime, 1789 publications have been published within this topic receiving 42864 citations. The topic is also known as: meteorological station & meteorological observation post.

A Deterministic Model for Predicting Hourly Dissolved Oxygen Change: Development and Application to a Shallow Eutrophic Lake

Abstract: Predicting dissolved oxygen (DO) change at a high frequency in water bodies is useful for water quality management. In this study, we developed a deterministic model that can predict hourly DO change in a water body with high frequency weather parameters. The study was conducted during August 2008–July 2009 in a eutrophic shallow lake in Louisiana, USA. An environment monitoring buoy was deployed to record DO, water temperature and chlorophyll-a concentration at 15-min intervals, and hourly weather data including air temperature, precipitation, wind speed, relative humidity, and solar radiation were gathered from a nearby weather station. These data formed a foundation for developing a DO model that predicts rapid change of source and sink components including photosynthesis, re-aeration, respiration, and oxygen consumption by sediments. We then applied the model to a studied shallow lake that is widely representative of lake water conditions in the subtropical southern United States. Overall, the model successfully simulated high-time fluctuation of DO in the studied lake, showing good predictability for extreme algal bloom events. However, a knowledge gap still exists in accurately quantifying oxygen source produced by photosynthesis in high frequency DO modeling.

Capability of a regional climate model to simulate climate variables requested for water balance computation: a case study over northeastern France

Abstract: This paper documents the capability of the ARW/WRF regional climate model to regionalize near-surface atmospheric variables at high resolution (8 km) over Burgundy (northeastern France) from daily to interannual timescales. To that purpose, a 20-year continuous simulation (1989–2008) was carried out. The WRF model driven by ERA-Interim reanalyses was compared to in situ observations and a mesoscale atmospheric analyses system (SAFRAN) for five near-surface variables: precipitation, air temperature, wind speed, relative humidity and solar radiation, the last four variables being used for the calculation of potential evapotranspiration (ET0). Results show a significant improvement upon ERA-Interim. This is due to a good skill of the model to reproduce the spatial distribution for all weather variables, in spite of a slight over-estimation of precipitation amounts mostly during the summer convective season, and wind speed during winter. As compared to the Meteo-France observations, WRF also improves upon SAFRAN analyses, which partly fail at showing realistic spatial distributions for wind speed, relative humidity and solar radiation—the latter being strongly underestimated. The SAFRAN ET0 is thus highly under-estimated too. WRF ET0 is in better agreement with observations. In order to evaluate WRF’s capability to simulate a reliable ET0, the water balance of thirty Douglas-fir stands was computed using a process-based model. Three soil water deficit indexes corresponding to the sum of the daily deviations between the relative extractible water and a critical value of 40 % below which the low soil water content affects tree growth, were calculated using the nearest weather station, SAFRAN analyses weather data, or by merging observation and WRF weather variables. Correlations between Douglas-fir growth and the three estimated soil water deficit indexes show similar results. These results showed through the ET0 estimation and the relation between mean annual SWDI and Douglas-fir growth index that the main difficulties of the WRF model to simulate soil water deficit is mainly attributable to its precipitation biases. In contrast, the low discrepancies between WRF and observations for air temperature, wind speed, relative humidity and solar radiation make then usable for the water balance and ET0 computation.

National Drought Insurance for Malawi

Abstract: Malawi has experienced several catastrophic droughts over the past few decades. The impact of these shocks has been far reaching, and the resulting macroeconomic instability has been a major constraint to growth and poverty reduction in Malawi. This paper describes a weather risk management tool that has been developed to help the government manage the financial impact of drought-related national maize production shortfalls. The instrument is an index-based weather derivative contract designed to transfer the financial risk of severe and catastrophic national drought that adversely impacts the government's budget to the international risk markets. Because rainfall and maize yields are highly correlated, changes in rainfall -- its timing, cumulative amount, and distribution -- can act as an accurate proxy for maize losses. An index has been constructed using rainfall data from 23 weather stations throughout Malawi and uses daily rainfall as an input to predict maize yields and therefore production throughout the country. The index picks up the well documented historical drought events in 2005, 1995, 1994, and 1992 and a weather derivative contract based on such an index would have triggered timely cash payouts to the government in those years. This innovative risk management instrument was pioneered in 2008/2009 by the Government of Malawi, with the assistance of the World Bank, and was a first for a sovereign entity in Africa. Several piloting seasons will be necessary to understand the scope and limitations of such contracts, and their role in the government's strategy, contingency planning, and operational drought response framework.

Combining observations and model data for short-term storm forecasting

Abstract: This paper describes the use of a machine learning data fusion methodology to support development of an automated short-term thunderstorm forecasting system for aviation users. Information on current environmental conditions is combined with observations of current storms and derived indications of the onset of rapid change. Predictor data include satellite radiances and rates of change, satellite-derived cloud type, ground weather station measurements, land surface and climatology data, numerical weather prediction model fields, and radar-derived storm intensity and morphology. The machine learning methodology creates an ensemble of decision trees that can serve as a forecast logic to provide both deterministic and probabilistic estimates of thunderstorm intensity. It also provides evaluation of predictor importance, facilitating selection of a minimal skillful set of predictor variables and providing a tool to help determine what weather regimes may require specialized forecast logic. This work is sponsored by the Federal Aviation Administration's Aviation Weather Research Program. Its aim is to contribute to the development of the Consolidated Storm Prediction for Aviation (CoSPA) system, which is being developed in collaboration with the MIT Lincoln Laboratory and the NOAA Earth System Research Laboratory's Global Systems Division. CoSPA is scheduled to become part of the NextGen Initial Operating Capability by 2012.

Road Surface Condition Forecasting in France

Abstract: A numerical model designed to simulate the evolution of a snow layer on a road surface was forced by meteorological forecasts so as to assess its potential for use within an operational suite for road management in winter. The suite is intended for use throughout France, even in areas where no observations of surface conditions are available. It relies on short-term meteorological forecasts and long-term simulations of surface conditions using spatialized meteorological data to provide the initial conditions. The prediction of road surface conditions (road surface temperature and presence of snow on the road) was tested at an experimental site using data from a comprehensive experimental field campaign. The results were satisfactory, with detection of the majority of snow and negative road surface temperature events. The model was then extended to all of France with an 8-km grid resolution, using forcing data from a real-time meteorological analysis system. Many events with snow on the roads were simulated for the 2004/05 winter. Results for road surface temperature were checked against road station data from several highways, and results for the presence of snow on the road were checked against measurements from the Meteo-France weather station network