1. Who Farmers looking to use precip in forecasting crop yield.
2. Why? The whole point to farming is getting a good yield. IF you had a tool to forecast crop yield, it could influence futures markets all the way down to the family budget.(e.g. if the yeild is predicted small then the famliy has to strech the budget.)
3.What is needed? Well you will need a forecast for precip. The key is to have a index to compare the value forecasted.
4. Lead time? This would definitely be a seasonal forecast. the time period would be over the growing season. The only problem I see with that is if it is really dry one month and really wet the next.
5. Value? The value will depend on the effect in the yield. If a large change in precip doesn't change the yield much, then the forecast will not matter much. If there is a large change in yield with a large change in precip then the forecast value would be high.
7. How to handle a marginal forecast? This kind of relates to 5. If a big change in yield is acompanied by big precip change, then you can look at a marginal forecast, and use its trend to forecast crop yield. Even if the change in precip is larger than the yield change.
Fisheries is one of the most important industries in the world. It can be a very important income source for families or even countries. Also, it is an industry very sensitive to weather and climate. Most of the larger fisheries in the world are in the ocean, where there is a lack of observational data.
Daily (and Weekly) forecasts are important because the affect decisions to sail or not. However, the population of fish and the fish catch do not vary very much from short-term perturbations but rather long-term changes such as seasonal variations. What would be our goals if we're going to work on seasonal forecasting? Here are some possibilities:
After searching for web-sites about fishery and seasonal forecast, we could not find any agency or organization which gives a forecast purely for fisheries. Therefore, we list items which can be used for fisheries:
Both global and regional seasonal forecast of sea surface temperature, air temperature and precipitation. People can get this information from NOAA or NCEP, for example, very easily.
* Real-time meteorological and oceanographic data from moored buoys.
* Long-term measurements of sea surface temperature and salinity at coastal stations
* Long-term monitoring of ocean properties
* Southern Oscillation Index
ALSO:
- Circulation & current systems (sea surface wind)
- Temparature & salinity
- Ocean waves (currents)
- Sea ice
- Ocean chemistry
- Pollutant sources, pathways & effects
- Ocean productivity
- Fish and shellfish habitat needs
+ Nautical charts
+ Tide tables
+ Sailing directions
+ Cruising atlases
+ Current atlases
+ Coastal, continental shelf & deep ocean modelling
+ State-of-the art oceanographic and hydrographic charts
For the case of the Peruvian fishery, SST monitoring would be very useful for their seasonal forecast. Upwelling decreases will reduce the fish population most because nutritients wil decrease. Therefore, information from the ocean should be most important. For the U.S., we can also use this index to monitor and then prepare for possible change of fish species.
1. Who? fire weather forecasters, forest fire management officials, state and national parks, homeowners living in wooded areas, possible tourists, logging companies.
2. Why? Fire weather forecasters, forest fire management officials and state and national parks would all be interested in knowing what type of conditions can be expected in the coming months. This knowledge would allow them to prepare for possible forest fire events by building up resources such as fire fighters, water bucket helicopters and slurry-drop airplanes. Homeowners would want to be informed if they should expect to be evacuated from their homes in the ensuing months. Tourists can decided from the seasonal forecasts whether or not they might want to visit a particular forested location. Logging companies would want to know if they could expect to harvest an economical amount of timber from a particular area by avoiding possible engagements with fire. Logging companies would also want to know prime locations for logging timber so as not to place the lives of their employees in danger.
3. What is needed? Temperature and precipitation will be the key factors. But also sustained wind speed and directions, probability of dry lightening to ignite new fires, and fuel moisture levels of the forest might also be included with the forecast.
4. Required lead time? 3-4 months prior to the fire season (July-October)
5. What value does forecast have? For the fire forecasts and parks officials the forecast merely instructs them where to expect danger and where to pool resources. But to homeowners, possible tourists and logging companies, the forecasts could give them the heads-up as to where fire danger might be highest. This would allow them to avoid these areas and prevent unnecessary loss of life.
6. How small of a scale does the forecast need to be down to? Is over- cautiousness going to cause unnecessary panic? There might be a way to refine the forecasts so that the scale results in a happy-medium between the two. Maybe a forecast domain of the extent of state counties up to state-wide.
7. How to use incomplete or marginally accurate forecasts to reduce risks? By looking at trends from previous fire seasons and the forecasts issued for them, the current situation could be extrapolated to build from the current knowledge of any forecast.
Our team discussed the importance and usefulness of accurate seasonal forecast products designed for petroleum industries. Sources were presented which focused specifically on cold season temperature products. The accuracy of temperature forecasts needed by oil companies can be understood by examining products offered by climitological prediction firms. One of these firms, for example, offers a detailed temperature outlook which on average is more accurate than the prevailing climitology for a given region.
It was noted that oil prices generally have an inverse relationship with temperature. A cold period can create a high demand for heating oil and thereby increase the oil price. One of the most useful temperature products discussed was heating degree days. Heating degree days allow oil companies to get a good idea of how much need there will be for oil in the coming cold season.
A way oil companies can help protect themselves from profit loss during a predicted warm winter is to purchase weather derivatives. Depending on how many weather derivatives are bought, oil companies are reimbursed for a low number of actual heating degree days. If, however, the cold season turns out to be colder than anticipated the weather derivatives bought by the oil companies can not be redeemed. The accuracy of seasonal temperature forecasts are therefore important for the end user as well as the forecaster.
1. Users: Researchers (ex. polar researchers)
2. Why: So they can decide whether or not to go on site to conduct the research
3. What: temps, winds, precip, severe weather potential, extreme climate
4. Lead time: 6-9 months
5. Value: Could save or endanger the lives of scientists or affect the quality of data collected
6. Other: ?
7. Use incomplete information to reduce risk: A heads up regarding much colder than normal temps could affect whether they can withstand the weather, thus whether they should wait another year. They don't need actual values, only a trend will save them
We used four basic sources to find the information on how weather affects the load of power plants: books, Internet, interviews with professors, and an interview with the ISU Power Plant. The most influential weather factors for power plants are temperature, wind, cloud cover, and precipitation. How fast the temperature changes is most important; it can affect the use of heaters and air conditioners. Temperature also depends on the time of day. It is usually coolest in the early morning and warmest in the middle of the afternoon. However, brick buildings (such as on campus) absorb the heat and insulate the building for a period of time, lessening the load required to heat it. An increase of wind speeds also increases the use of power. Cloud cover causes darkness thereby increasing the use of lighting in homes and businesses which increases the load, as well. Precipitation can be related to cloud cover and cooler temperatures, but it is also important for water-generated power plants. Power plants want to know that they will have the necessary supply of water to provide enough power for their customers.
Lead-time is somewhat different for each weather factor. Daily weather forecasts are usually given three times a day (but sometimes every three hours.) They are used to predict daily power demands, especially during peak hours. Heating and cooling degree days are also useful to predict the cost of heating. Weather forecasts for precipitation (wet, normal or dry season) are valued one season in advance. Another seasonal forecast that is important is El Nino's projected impact on the power demand.
1.- Who? Weather Risk Companies
2.- Why? To buy/sell weather derivatives. This hedges a buyer (such as an energy company, a farmer, a ski resort) against losses due to bad (depending on who) weather conditions. The risk management firm that sells the derivatives makes a profit when the weather is near normal and does not reach a predetermined level to where they have to payout for the buyer's loss.
3.- What is needed (including accuracy)? Precipitation, Snowfall, Snow Depth, Temperatures, Cooling/Heating Degree Days, Wind. All of these parameters within a certain (such as 0.8) standard deviation from the mean (assuming a Gaussian distribution)
4.- Required lead time? 6 months to a year
5.- What value does forecast have? Can mean millions of dollars in either profit/loss for a weather risk company and for the industry involved in the transaction.
6.- Other relevant questions? How accurate can a long-range outlook be to give a weather risk company maximized profit?
7.- How would they use incomplete or marginally accurate forecasts to reduce risk? They could still make a contract using climatology and then hedging on extreme events. The weather risk company would probably lose/not make as much profit with inaccurate forecasts, since their threshold on hedging would not be as good.
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