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Rainwatch
A Prototype GIS for Rainfall Monitoring in West Africa
This paper describes Rainwatch, a stand-alone, prototype Geographic Information System (GIS) application that automates and streamlines key aspects of rainfall data management, processing, and visualization for West Africa. Rainwatch is an interactive Map Objects Visual Basic application that permits the tracking of critical rainfall attributes beneficial to farmers. Using the simple-to-understand concept of cumulative rainfall plots, the program allows users to compare rainfall for any year against six percentile thresholds for a historical reference period (1965-2000). These thresholds separate dry, normal, and wet conditions. Users also can compare rainfall data between stations for a given season or between seasons for a particular station, and spatially interpolate rainfall for a single event, defined period, or an entire season. The system is dynamic and automatically updates all charts and tables as new data are added to the database. Thus, for this poor and drought-prone region, Rainwatch can help reduce delay in rainfall data processing, facilitate communication between data collection agencies, and generally make rainfall data more accessible and meaningful.
This paper describes Rainwatch, a stand-alone, prototype Geographic Information System (GIS) application that automates and streamlines key aspects of rainfall data management, processing, and visualization for West Africa. Rainwatch is an interactive Map Objects Visual Basic application that permits the tracking of critical rainfall attributes beneficial to farmers. Using the simple-to-understand concept of cumulative rainfall plots, the program allows users to compare rainfall for any year against six percentile thresholds for a historical reference period (1965-2000). These thresholds separate dry, normal, and wet conditions. Users also can compare rainfall data between stations for a given season or between seasons for a particular station, and spatially interpolate rainfall for a single event, defined period, or an entire season. The system is dynamic and automatically updates all charts and tables as new data are added to the database. Thus, for this poor and drought-prone region, Rainwatch can help reduce delay in rainfall data processing, facilitate communication between data collection agencies, and generally make rainfall data more accessible and meaningful.
Weather Forecast Uncertainty Information
An Exploratory Study with Broadcast Meteorologists
A four-day educational cruise navigated around the leeward side of Oahu and Kauai to observe the thermodynamic and dynamic features of the trade-wind wakes of these small islands by using weather balloons and other onboard atmospheric and oceanographic sensors. This cruise was proposed, designed, and implemented completely by graduate students from the School of Ocean and Earth Science and Technology at the University of Hawaii. The data collected during the cruise show, for the first time, strong sea/land breezes during day/night and their thermal effects on the island wake. This cruise provided the students with a significant, valuable, and meaningful opportunity to experience the complete process of proposing and undertaking field observations, as well as analyzing data and writing a scientific article.
A four-day educational cruise navigated around the leeward side of Oahu and Kauai to observe the thermodynamic and dynamic features of the trade-wind wakes of these small islands by using weather balloons and other onboard atmospheric and oceanographic sensors. This cruise was proposed, designed, and implemented completely by graduate students from the School of Ocean and Earth Science and Technology at the University of Hawaii. The data collected during the cruise show, for the first time, strong sea/land breezes during day/night and their thermal effects on the island wake. This cruise provided the students with a significant, valuable, and meaningful opportunity to experience the complete process of proposing and undertaking field observations, as well as analyzing data and writing a scientific article.
Forecast verification in operational hydrology has been very limited to date, mainly due to the complexity of verifying both forcing input forecasts and hydrologic forecasts on multiple space-time scales. However, forecast verification needs to be the driver in both hydrologic research and operations to help advance the understanding of predictability and help the diverse users better utilize the river forecasts. Therefore, in NOAA's National Weather Service, the Hydrologic Services Program is developing a comprehensive river forecast verification service to routinely and systematically verify all hydrometeorological and hydrologic forecasts. This verification service will include capabilities for archiving forecast and observed data, evaluating logistical properties of the forecast services, computing a variety of verification metrics to evaluate the different aspects of forecast quality, displaying and disseminating verification data and metrics, and analyzing the sources of forecast skill and uncertainty through the use of multiple forecast and hindcast scenarios. This paper describes ongoing and planned verification activities for enhancing the collaboration between the meteorological and hydrologic research and operational communities to quantify forecast improvements based on rigorous forecast verification.
Forecast verification in operational hydrology has been very limited to date, mainly due to the complexity of verifying both forcing input forecasts and hydrologic forecasts on multiple space-time scales. However, forecast verification needs to be the driver in both hydrologic research and operations to help advance the understanding of predictability and help the diverse users better utilize the river forecasts. Therefore, in NOAA's National Weather Service, the Hydrologic Services Program is developing a comprehensive river forecast verification service to routinely and systematically verify all hydrometeorological and hydrologic forecasts. This verification service will include capabilities for archiving forecast and observed data, evaluating logistical properties of the forecast services, computing a variety of verification metrics to evaluate the different aspects of forecast quality, displaying and disseminating verification data and metrics, and analyzing the sources of forecast skill and uncertainty through the use of multiple forecast and hindcast scenarios. This paper describes ongoing and planned verification activities for enhancing the collaboration between the meteorological and hydrologic research and operational communities to quantify forecast improvements based on rigorous forecast verification.
It goes without saying that most crops are sensitive to variations in weather and climate. When the influence of the El Niño-Southern Oscillation on rainfall for several regions of the world was first discovered climate scientists assumed that this information would be of immediate use by farmers, the general agricultural community, and other communities. One of the reasons this has not happened as quickly and universally as expected is that many users are not able to relate the climate information to their practices. In this paper we illustrate an application of climate information as it relates to the risk of a poor crop yield. Specifically we show how the odds of a good, versus poor, maize yield are tilted by variation in the seasonal climate. In addition, we illustrate strategies that could allow farmers a way to manage the climate risk associated with these shifts. We illustrate maize-yield sensitivity to seasonal climate by simulating the influence of relatively small variations in dry spell duration on maize yields in Uruguay.
We then show that the observed median dry spell durations in the maize-growing season of Uruguay during El Niño and La Nina episodes are in the range where maize yields are most sensitive to dry spell length. Variations in maize yields developed from the crop model are consistent with observed mean yields and with observed yields during El Niño and La Nina conditions. Finally, we discuss risk management strategies based on cultivar and planting dates.
It goes without saying that most crops are sensitive to variations in weather and climate. When the influence of the El Niño-Southern Oscillation on rainfall for several regions of the world was first discovered climate scientists assumed that this information would be of immediate use by farmers, the general agricultural community, and other communities. One of the reasons this has not happened as quickly and universally as expected is that many users are not able to relate the climate information to their practices. In this paper we illustrate an application of climate information as it relates to the risk of a poor crop yield. Specifically we show how the odds of a good, versus poor, maize yield are tilted by variation in the seasonal climate. In addition, we illustrate strategies that could allow farmers a way to manage the climate risk associated with these shifts. We illustrate maize-yield sensitivity to seasonal climate by simulating the influence of relatively small variations in dry spell duration on maize yields in Uruguay.
We then show that the observed median dry spell durations in the maize-growing season of Uruguay during El Niño and La Nina episodes are in the range where maize yields are most sensitive to dry spell length. Variations in maize yields developed from the crop model are consistent with observed mean yields and with observed yields during El Niño and La Nina conditions. Finally, we discuss risk management strategies based on cultivar and planting dates.
