Red sky at morning, sailor take warning.

Red sky at night, sailor’s delight

Old proverbs such as this were the earliest weather forecasts. Passed down from generation to generation, these rhymes helped our ancestors recognize when a storm was brewing or a cold snap was on the way.

Long before Doppler radar, farmers and sailors in Colonial America knew how to predict the weather by reading the wind and watching the skies. Their approach to reading the weather is just as useful in today’s world.

Watching the Clouds

Red sky at morning, sailor take warning.

Red sky at night, sailor’s delight

This is perhaps the best-known weather proverb, but what does it mean, exactly?

The type and color of clouds in the sky can be an excellent weather predictor. When the sun rises or sets, its light takes on a reddish tint as it is filtered through the atmosphere.

To have a red sky in the morning, the eastern sky needs to be clear of clouds and the western sky must be cloudy. Since the prevailing wind direction in the northern hemisphere is west to east, a red sky at morning means that storm clouds are headed your way.

Likewise, a red sky at night means that the clouds are to the east and clear skies to the west, all of which meant that you can expect a calm night.

Halo around the sun or moon,

Means rain or snow soon

This old rhyme can forecast precipitation within the next 24 hours. There are several different types of clouds, and the appearance of some is a pretty good sign that rain or snow are on their way.

A halo around either the sun or the moon is caused by ice crystal floating inside cirrostratus clouds. The ice crystals refract light, which will cause the halo mentioned in this proverb.

Cirrostratus clouds are very thin, high altitude clouds that give the sky a milky white appearance. If you see cirrostratus clouds, you can indeed expect rain or snow within 24 hours.

Written in the Wind

“He doesn’t know which way the wind is blowing.”

This old saying means that a person is a fool who can’t see an obvious trend.

Knowing the wind direction was vital to the lives of farmers and anyone working outdoors. That’s why every early American farmhouse featured a weathervane. The direction your weathervane pointed could warn a coming storm or tell if it was a good day to work the fields.

Colonial Americans loved weathervanes and adapted them into beautiful, ornate designs, including flying eagles, angels and leaping fish. In part these designs were aesthetic; in part they were functional, since they made the weathervane easier to read from across the fields.

So what does wind direction tell us? This proverb gives an idea.

When the wind is blowing in the North

No fisherman should set forth.

When the wind is blowing in the East,

‘Tis not fit for man nor beast.

When the wind is blowing in the South

It brings the food over the fish’s mouth.

When the wind is blowing in the West,

That is when the fishing’s best!

In the eastern half of the United States the prevailing winds blow from the west. A shift in wind direction toward the east or north means bad news. It could even mean that a dreaded “Nor’easter” was approaching.

Keep that in mind next time you see a weathervane. It might tell you whether you need your umbrella soon.

Sometimes I feel sorry for the local weatherman. He or she has to get in front of the television camera every day and give forecasts up to a week in advance knowing full well that those forecasts are apt to be wrong. Here’s why.

Weather forecasting is based on computer modeling and these computer models are based on two things; the physics and the data. Each model that the forecaster can choose from is based on different physics assumptions, sometimes very different.

For example, will a storm just entering the west coast of the United States travel across the continent without any intensification and exit the east coast three days later? Or, will it slowly intensify as the days go on and exit the east coast as a major noreaster? Each computer model handles the situation differently and the forecaster must choose which one to use.

Quite often the atmosphere is in a state which is a combination of several different physical models, not just one. In this case, it is not at all unusual for the computer models to predict wildly differing forecasts for three to seven days into the future. When this happens, the best the forecaster can do is to put all the options out on the table. Of course, this is not what the public wants to hear and a single forecast must be made. When the inevitable happens and the forecast is wrong the weatherman must indeed have a thick skin if he is to withstand the barrage of jokes and insults which will surely come his way.

As if that is not enough, here is the real problem. These forecasting models are based, of course, on the data that is input into the computer program. This data is a compilation of current weather observations and this data is VERY incomplete. As an example, suppose two observations are fifty miles apart. The observers diligently compile the relevant weather statistics and send them off to the national weather service where the data is then input into each computer model.

Here is the problem. In the fifty miles seperating the observers, there might be some weather phenomena that is going, through no fault of anyone, unreported. This may be something very small but as the program extrapolates out days and weeks ahead, that little something may, and usually does, have a huge effect on the weather that will be seen in the forecast area.

It is known as the “butterfly effect”( a butterfly flapping his wings halfway aroung the world will eventually have an effect on the weather seen locally), and it is the dirty little secret of weather forecasting. Until we are able to collect data in a close to continuous fashion between observation points there is very little chance that our weather forecast a week ahead will be correct and nobody sees this possibility occuring in the near future.

So the next time you see or hear your local weather forecaster speaking confidently about next week’s forecast, know that deep inside he or she is grimacing and taking that forecast with a whole bushel full of salt. And please, hold all those weather jokes!

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Real time satellite images can make forecasting weather conditions much more accurate because they show exactly what is happening in any given location. Satellite images show the intensity, speed and direction of storms, allowing a forecaster to give more accurate information to the areas in front of the storm.

By observing a satellite image, the forecaster can see what changes are taking place within the storm system over a period of time. If the storm is intensifying or breaking apart, changing directions, speeding up or slowing down, this will all become apparent in real time satellite images. This becomes especially beneficial in areas prone to hurricanes or tornados. The satellite images allow forecasters to predict with relative accuracy what areas may have the greatest threat of being hit by an approaching storm, thus allowing the local people to prepare.

A satellite image can also help forecasters determine the severity of the approaching storm. They can forecast heavy showers or snow storms, or light rain or flurries according to what the satellite images show. They can also see where severe conditions may be forming with in a larger less severe system.

This information can be used for many things besides preparing people for approaching weather conditions. Have you even seen awesome pictures of storms? Satellite images can help photographers determine where to set up to get some of those incredible storm or lightning pictures you’ve seen. By observing the location of severe weather cells within storms, photographers are able to determine if they will have a good chance to capture tornado, cloud formation or lightning pictures. They can also forecast when and where the safest places for taking these pictures will most likely be.

Real time satellite images have made weather forecasting much more accurate and useful than it was not so many years ago because of the details and amount of information available. Forecasters are able to give the public a much better picture of what to expect in the next week, day or hour because of information provided by satellite images.

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With something as unpredictable as the Forex market, myths that suggest that it is anything other than a labyrinth with a Minotaur around every corner are dangerous. Given the odds, it is almost impossible for a new trader with zero knowledge of the market to get lucky and make their fortune. They are almost certain to crash and burn without expert guidance yet the proliferation of myths regarding foreign exchange rates make it more likely that they will try and beat the market with no knowledge anyway.

Actually the above title is extremely misleading. The weather can and usually is predicted with a reasonable degree of accuracy. Foreign currency exchange rates on the other hand are nigh on impossible to figure out. Even the most successful traders will lose quite frequently but the difference between them and losing traders is the fact that top quality traders usually win big when they do win and lose small on the many occasions they get it wrong. Although the fact that 90% or more Forex traders lose is a figure skewed by the amount of new traders who never win, the market is not and never will be predictable enough for anyone to guess correctly even half the time.

Even though it is well known that the Forex market is basically a game of chance with unfavorable odds for all but experienced traders, newcomers still insist on spending big and incurring huge losses. The cautionary tales that surround the market get ignored in favor of the belief that today will be the trader’s lucky day. This means that despite all evidence to the contrary, even in light of the fact that the Forex market is impossible to predict, traders still elect to follow myths rather than common sense.

One of these myths surrounds the effectiveness of Forex software. Yes, there are indeed some good quality robots available that may make you a small profit in the long run if you’re patient. However, most of these machines are piles of garbage with their reputation based on meaningless back testing. The figures they come up with are completely useless because they are simulations with prior knowledge of the closing price. Then you have people who think that complicated systems trump all else. Again, while it is true that a well-thought out complex system can indeed be relatively successful, the mere fact that they have more elements to break than moderate level systems is usually their downfall.

Possibly the strangest myth is the one that suggests that the best way to win is to trade often. This has no bearing on whether you will win or not. All that will happen here is that you’ll lose even more money if you choose incorrectly. The most lucrative trades come around rarely and these are the key to Forex success. While you must listen carefully to all news concerning the various factors that influence the Forex market, you need to remember that the market reacts to trader’s reactions not necessarily the news. Excited or panicky reactions are what affect the market, not the news that has been broadcast, as bizarre as that sounds.

With such a volatile market, following your instincts is not recommended. As difficult as it is to stay calm while chaos rages around you, the cool and collected trader will always have a chance of emerging victorious from the unpredictable Forex market.

It’s not easy to forecast the forex markets, but it’s what thousands of forex traders and brokers do every day, with varying degrees of success.

Like forecasting the weather, predicting the forex market is sometimes a crapshoot, sometimes a guessing game, and always an adventure. There are two basic philosophies on how to forecast the forex markets. One is technical analysis; the other is fundamental analysis. We’ll look at them both.

Natalia Osorio Editor of the “Best Forex Trading” website — http://www.BestForexTradingUsa.com — pointed out;

“…The technical approach examines past market action and uses that data to predict the future. Previous trends in most areas of life are almost always good indicators of the future; forex is no different. People have not changed much in the decades since the forex market was created. People still buy and sell and react to stimuli in much the same way as they did 50 years ago.

Since forex rates change constantly throughout the day, every day, looking at all the years of past data can be daunting. Smart analysts learned to look at the big picture, to skip the minor details and examine trends over a longer period of time…”

Using fundamental analysis to forecast forex markets is a bit more in-depth, but it can also be highly accurate. Basically, fundamental analysis means forecasting the market based on external factors — political moves, government involvement, social movements, even the weather. Someone good at fundamental analysis might forecast forex drop-offs because he knows a country’s government is unstable at the moment, or increases because the country has just elected a popular new leader. Anything that can affect a nation’s economy can affect the exchange rates, and that’s what a fundamental analyst uses to guess at the forex market’s future

Naturally, this means having to know a particular country in-depth, which is hard to do for more than a few countries at a time. (It becomes even more complicated when trying to forecast the euro, since several different countries use that currency.) But having that kind of intricate knowledge makes it much, much easier to forecast forex trends.

“…Most good traders use a mixture of both processes, technical and fundamental. For example, a trader might see that a country is currently facing a particularly strong hurricane season (fundamental) and know that in the past, strong hurricane seasons have meant a weaker economy for that nation (technical). Thus, he can predict down-turns for that nation with some degree of confidence…” N. Osorio added.

Further Information About The Best Forex Trading Softwares And Resources  By Visiting; http://www.BestForexTradingUsa.com

Some provinces in the Philippines are fortunate when the weather takes a turn for the worse. They are given ample warning since storm effects will hit the Pacific Ocean facing provinces first before it can reach them. Those who have many mouths to feed must wring a living from the sea despite warnings but blue water fishermen scoot for the shore fast when the wind rises. Some do not go to sea at all. Those ashore have ample time to batten down before the winds arrive.

Others are not so fortunate. They are washed away by flashfloods or buried by landslides. Some get caught in the open sea. They are either marginal fishermen or ship passengers whose ships had sailed expecting to make the port before the storm hits. Fishermen rely on radio and TV news for weather forecasts while ships rely on the PAGASA, the Philippine Weather Bureau. The reports seem to be not up to date since there are those who were caught by surprise.

Ever since, man has to protect himself and his possession from the weather. However, there is no such thing as sure protection, there is only forecasting so he can do something to make himself and his property safe. Though the eleven nations composing the International Geophysical Cooperation say that weather forecasting cannot still be highly accurate, it can still be predictable within limits. That means during adverse condition, the weather must be observed closely so abrupt changes may be broadcasted immediately.

The doomed Princess if the Stars ship sailed thinking that her sailing route was well away from the typhoon path. The storm veered abruptly and wrecked the ship. The tragedy was blamed on the PAGASA but the weather bureau said that it does not have the facilities for monitoring the progress of the storm. Until now any disaster caused by the weather is blamed on the PAGASA but their instruments are obsolete and their organization is non existent.

Since weather knows no boundaries, nations had cooperated in sharing weather information. We do not have a geosynchronous weather satellite but we can have international reports of the nations in or bordering the Pacific. We can also have weather reports of the incoming and outgoing international pilots. But that information can only give us the weather picture away from the Philippines. Once the weather enters our area of responsibility weather reports slow down.

The PAGASA director said their equipment is obsolete so they cannot closely monitor conditions here. Storms entering the Philippines follow the general direction of southeast to northwest route. However, it may be diverted by atmospheric pressures over the country causing to veer from the predicted path. Without weather reports updated by the hour during storms, changes of direction cannot be accurately predicted. The weather bureau would need the hourly reports of digital barometers, wind gages and wind vanes, thermometers, and hygrometers strategically places all over the country.

Properly interpreted and recorded, the readings would not only locate the precise location of the storm but also provide a fairly accurate and timely prediction of the path of the turbulence. The isobaric map would show high and low pressure areas that would possibly affect the progress of the storm so hourly information could be reported. For data gathering and information dissemination the Departments of Agriculture, Environment and Natural Resources, Army, Navy, Air Force and Coast Guard facilities of National Defense may be utilized.

As had been said before, there is no real protection of lives and property against storms. Relatively accurate forecasting could institute damage prevention activities. Having our own geosynchronous weather satellite is out of the question. For reasonably accurate forecasting, proper and up to date equipment must be purchased and installed.

Typhoon is either a weekly or a monthly reality in the Philippines, the country being located east of the Pacific Ocean. Maybe in the future, a software can be installed in one’s computer and it will make realistic predictions of storm paths.

When retailers seek help with issues relating to inventory management, they are usually concerned about an increasing level of out-of-stocks, which are leading to lost sales and customer service complaints, or over-stocks, which are resulting in slow inventory turnover and a build up of dead inventory. In fact, out-of-stocks and over-stocks are actually the flip side of the same inventory management coin.

Any effective initiative to resolve these issues must address the core structural causes of these inventory management problems. Superior inventory management begins with timely, accurate, detailed demand forecasts.

It is critical to differentiate between demand planning and purchase planning. Demand planning is the sales plan from which inventory planning, purchase planning and replenishment parameters are built. It is impossible to plan inventory and purchasing activities or build replenishment parameters without a detailed forecast of what will be sold, how much will be sold, when it will be sold, the channels it will be sold through, and who the ultimate customers will be. And yet, all too frequently replenishment parameters are rolled over, existing purchasing patterns continue, and inventory is allowed to ebb and flow as if on auto- pilot. The result is out-of-stocks and over-stocks as demand changes.

Without highly reliable forecasts, retailers must attempt to strike a delicate balance between carrying too little or too much stock. Frequently, they feel compelled to protect themselves against out-of-stocks and backorders by stocking layers of additional inventory in reserve, unnecessarily tying up valuable resources that could be used in more productive ways to serve customers and grow the business. 

Review Historical Sales Data
Accurate demand planning and forecasting begins with a thorough review of historical sales data. It is critical that sales not made from stock, special orders, large closeout sales and any other extraordinary sales be excluded from this historical data. Most demand planning and forecasting software packages will exclude these sales if the forecasting software is fully integrated with order management software, and those excluded orders have been properly tagged or exclusion parameters have been loaded into the system. It’s also critical that lost sales due to out-of-stocks are also factored in so that the history reflects actual demand rather than just sales.

It is important that the planning process drills down to the lowest possible level so that every category, sub- category, style or SKU is reviewed not just for potential opportunities and current sales trends, but also for the potential negative impacts of increased competition, emerging technology, changes in promotional patterns and new product introductions. For distributors and wholesalers this may mean planning at the individual SKU level. Planning can be further refined by breaking key categories and items down by customer type, key customer, and even key customer by shipping location. Important sales trends, both positive and negative can be identified, and important historical events, such as unusual local weather, can be taken into account.

Once the historical sales data has been reviewed and adjusted, the data will frequently be averaged or smoothed to eliminate any remaining fluctuations in the sales pattern. Smoothing, however, can often lead to problems if not done carefully. For instance, using a three week moving average to smooth weekly historical sales may lead to out-of-stocks or over-stocks if sales are typically heavy at the beginning or end of each month. Utilizing monthly historical data rather than weekly data may seem like a reasonable way to simplify the planning process, but may in fact have the unintended consequence of smoothing historical sales in a way that may conceal meaningful sales patterns.

Understand Selling Characteristics
It is imperative to clearly understand the selling characteristics of each category, sub-category, item or SKU. These characteristics will determine the appropriate methodology for developing a forecast, as well as the level of detail required in the forecast. The most obvious characteristic is the degree of seasonality. Items which exhibit little sales fluctuation from month to month throughout the year require a very different forecasting methodology than items which exhibit significant seasonal sales fluctuations.

For seasonal items, most forecasting methods will start with the prior year’s sales by week or month, apply some smoothing technique, and then apply a current trend factor to arrive at a current year forecast for the corresponding time frame. For non-seasonal items, sales by week or month for the most recent weeks or months will be used as a starting point, smoothed and adjusted for the trend factor to arrive at a current forecast. In fact, it is very easy to completely overlook non-seasonal items when forecasting. It may seem sufficient to merely update replenishment parameters. A thorough analysis of non-seasonal items is necessary, however, to identify sales trends which may affect future sales volume, as well as to build an overall sales forecast.

Another characteristic which must be clearly understood is the sales velocity of an item. Sales velocity is defined as the number of orders an item generates over a given period of time. Items with high sales velocities generate a substantial number of orders during a given period of time, which makes their sales volume during that period more predictable than items with low sales velocities, which may only generate orders sporadically.

It is important to note that sales velocity is not the same as sales volume. For example, an item that generates 50 orders of 2 units each over a given period of time will have the same sales volume as an item which generates 2 orders of 50 units each, but the velocity of each item will be dramatically different. Clearly, the sales history of the item which generates 50 orders will lead to a forecast that will be more meaningful in the development of future inventory plans, purchasing needs and replenishment parameters than the sales history of the item which generates only 2 orders.

Many distributors group their items by sales volume using an A-B-C-D system. A items are those items which generate the vast majority of their sales volume, while B, C and D items generate increasingly smaller fractions of their sales volume. As a result, frequently these distributors will forecast and replenish their A items using one methodology, their B items another, and so on. However, while the grouping of A items may be made up primarily of high velocity items, every item will not necessarily be an A item. Conversely, while the grouping of D items will most likely be made up entirely of low velocity items, it is likely that within the B and C groupings that there will be a mix of both low and high velocity items. Utilizing sales velocity rather than A-B-C-D groupings to determine the appropriate forecasting methodology will result in forecasts that will result in fewer out-of-stocks and over-stocks.

Low velocity items may include supplementary items, which may be necessary to complete a given customer order for high velocity items, such as specialty ceramic tile trims to go with standard field tile. Low velocity items may also include complementary or customer convenience items, which are stocked so customers can purchase all of their needs in “one stop”. For low velocity items which exhibit an irregular sales pattern the forecast may reflect smoothed historical sales data, but that forecast would be less meaningful for actual replenishment than the average customer order quantity. As a result, the replenishment parameters would likely be calculated based on maintaining enough quantity in stock to support a given number of orders at the typical or usual sales order quantity.

Bottom Up versus Top Down Planning
As SKU’s are rolled up into sub-categories, and then into categories, the resulting planned sales increase can be evaluated in the aggregate at the total company level. This “bottom up” planning must be done in units. Regardless of what the actual unit of measure is, the obvious purpose of developing any demand plan or forecast is to provide the information necessary to build replenishment parameters, plan purchasing activities and issue actual purchase orders to vendors.

As the demand plan is being developed, however, unit plans must also be “dollared out.” As management assesses the overall market environment and the strategic opportunities and risks for the company, they will likely establish a financial budget, critical for cash flow forecasting, from the “top down”, which will be stated in dollars. As managers develop and roll up their forecasts, they must be careful that their “bottom up” unit plan remains in line with the financial “top down” dollar plan, and be prepared to adjust the unit plans accordingly.

Frequently, “bottom up” unit plans will forecast a sales increase significantly greater than the company’s “top down” financial budget. The reason for this is that in the course of building a “bottom up” unit plan far more items or categories are likely to be planned up than planned down. The natural tendency is to plan sales increases, especially in organizations with multiple buyers who are evaluated on their ability to generate sales increases with their items, categories and departments. Clearly, every item, category or department is not going to generate an increase, and companies which discourage their buyers from forecasting sales decreases are building in potential inventory problems right from the very beginning of the process.

Forecasts Need To Be Continually Updated
While demand planning and forecasting are generally thought of as a process that takes place at the beginning of each year or selling season, superior inventory management requires that forecasts remain dynamic and be continually updated to reflect the most current market conditions and sales trends. It does little good for a company to have taken the time to carefully forecast demand for the upcoming season or year, only to open the door to out-of-stocks or over-stocks by failing to update those forecasts on a continual basis. Static forecasts which have not been updated will invariably lead to faulty purchasing decisions.

Updating forecasts may be as simple as carefully monitoring the sales trend and updating the forward periods accordingly. In other cases there may be leading indicators that can be utilized to continually adjust the forecast. For those items or categories where customer orders are booked well in advance of actual ship dates, advance bookings may be able to be used as a leading indicator. In order for this to be an accurate indicator, however, prior year orders must be cross referenced between the period in which the order was booked, and the planned and actual ship date. Without a fairly sophisticated order management system to track this information, and very careful assessments of individual factors which may be impacting the timing of the placement of orders this year versus last year, utilizing advanced bookings to make significant adjustments to the forecast may by itself lead to variances between planned and actual sales, resulting in out-of-stocks or over-stocks.

A far more accurate leading indicator of sorts is, in fact, the demand forecast of a company’s customers. In fact, the closer any forecast is to the ultimate point of sale the more accurate and timely it will be.

Vertical information sharing throughout the supply chain is at the cutting edge of efforts to improve forecasting accuracy. The Collaborative Planning, Forecasting and Replenishment Committee is made up of retailers, manufacturers, and solution providers dedicated to this effort. It was formed to create collaborative relationships between buyers and sellers through shared information and co-managed processes. The Committee states that by “integrating demand and supply side processes CPFR® will improve efficiencies, increase sales, reduce fixed assets and working capital, and reduce inventory for the entire supply chain while satisfying consumer needs.” This group has developed a set of guidelines for developing business processes that enable collaboration across a number of buyer/seller functions.

The potential of collaborative forecasting is to finally fully rationalize the supply chain so that unnecessary inventories can be completely eliminated rather than inevitably building up with the company in the chain with the least economic leverage. In a supply chain where information is not shared, but, in fact, is closely held, it is inevitable that inventory risk will be pushed back by the companies with the greatest leverage onto the companies with the least. But the mere presence of excess, unnecessary inventory anywhere within the supply chain inflates costs for every member of the chain, and ultimately weakens the chain.

Measure and Analyze Variances Between Forecast and Actual
Finally, once a forecast has been developed, it is critical to measure its accuracy. It’s important to recognize that a forecast is just that, a forecast. There will always be variances between forecasted and actual demand. By measuring and then analyzing those variances, the factors that contribute to variances can be identified and strategies can be developed to account for them, so that future forecasts are that much more accurate, and variances minimized.

Conclusion
The greatest challenge to finally achieving superior inventory management, and maximizing the return on inventory investment, lies in developing accurate forecasts. Much work has been done over the past ten to fifteen years to rationalize processes in the supply chain, and eliminate unnecessary inventory. This has led directly to truly astounding cost saving and productivity gains. But for all the gains that have been made on the supply side of the inventory equation, the greatest opportunity for additional gains today is on the demand side. Not only does superior inventory management begin with accurate demand planning and forecasting, but making the commitment to developing accurate forecasts, continually updating them, and measuring their accuracy against actual sales also offers independent retailers the greatest opportunities today to maximize their return on inventory investment.

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When you want to know what the weather looks like this second, what do you do? Well, you have a couple of choices – turn on the Weather Channel or stick your head out the window. But when you want to know what the weather is most likely going to look like a week or a year from now, the latter options seem rather silly.

Unfortunately, many companies use the “stick the head out the window” method of gauging what’s happening now AS WELL AS what’s going to happen in the future. They just assume that the economic climate won’t change; as a result, their forecasts are way off the mark. This can mean death for your company.

If your organization is public (and even if it’s not!), you need to have someone on your team who can play “meteorologist”, essentially forecasting what your numbers will look like weeks and months from today. Highly trained CFOs can help in this area, as they have the know-how and experience to interpret changes.

Forecasting provides a prediction of your company’s future. It acts as a roadmap and enables management to make changes that will provide the best financial outcome. Specifically, a thorough forecast can help you answer the following:

1. Profit
2. Demand
3. Expenses
4. Cash ( how much money you need to borrow in order to operate your business and how to pay it back)

In today’s uncertain economic environment, forecasting may be the key to the success of your business. Of course, there are always unexpected catastrophes – let’s call them corporate hurricanes, tornadoes, floods and earthquakes. But you cannot let the possibility that they will occur stop you from hiring someone to build a solid forecast model which can be routinely updated.

Remember – when you have an educated and experienced CFO on your team, one who understands forecasting, you can weather practically any storm.

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Do you think better weather forecasting requires a degree in meteorology? Maybe a degree in statistical analysis would be more helpful. Consider this following new way to forecast the weather with more accuracy and less knowledge.

Friday, February 2, 2007 – Canon City, Colorado. I brought in my Canon City Daily Record from the porch when it arrived, at about 3 in the afternoon. I opened the newspaper to the page with the weather forecast, wondering how cold it would be the following day.

The paper forecast a high temperature was 13 degrees Fahrenheit. I knew this was way too low. Forecasts on television and on the internet said that we would reach 23 or 27 degrees the following day. I knew they were also too low, and I told my wife it would be in the 30s at least. The actual high temperature the next day was 53 degrees Fahrenheit.

No, that’s not a typo. The various weather forecasting “experts” were off by as much as 40 degrees – and that was for a simple 24-hour forecast. How could they be so far off? And how could I be better than them at forecasting the weather?

I can’t answer the first question. The weather here is more unpredictable than in most places. Also, perhaps the meteorologists follow there computer models too slavishly, even when their experience and intuition tell them to adjust the forecast.

However, I can answer the second question. My guess was closer because the forecasters were so consistent in the way they made their errors. Around this time, I remember counting something like 15 out of 20 days when all the various weather forecasts predicted a high temperature that was 5 degrees or more too low. All I needed to do was take the highest temperature forecast and add five degrees.

A New Weather Forecasting Model

Consistency in their errors was the key to my success. They weren’t forecasting too high one day and too low the next. They were wrong in the same ways over and over.

Are the errors as consistent in other parts of the country? That could be determined by looking at the statistics. Check the forecast highs and lows for the last 365 days, and check the actual temperatures for those days. See what the predicted probabilities of rain or snow were, and what actually happened.

Suppose that of the last 24 times a forecaster predicted a 50% chance of rain, it actually rained 18 times. He may have the best data, but he may be too conservative in how he uses it. Suppose this was not a fluke – which can be determined by doing more statistical analysis. You could know nothing about weather forecasting and provide a more accurate forecast simply by saying “A 75% chance of rain tomorrow” every time he said there was a 50% chance, right?

That’s the basis for this new forecasting model. First gather the statistical information on the forecasts of several weather forecasting services or meteorologists. Compare this to the actual weather that happened, and look for any consistencies in the inaccuracies. Then you create a computer program. As you enter each of these forecasts into it, they are adjusted for known tendencies. The result is a more accurate forecast.

If Forecaster A has managed over the last year to forecast a high that averages 4 degrees over the actual high, the computer adjusts for that. More sophisticated analysis might show that Forecaster B is consistently predicting a higher probability of rain than there is in the fall, but a lower probability of rain than there actually is in the spring. The computer can take this into account. Finally, it may work best if the adjusted forecasts of three or more sources are then averaged.

There really is no need to know anything at all about weather forecasting. This is based on the idea that even when experts have all the best knowledge and data, they sometimes apply it incorrectly, and do so in consistent ways. Don’t be surprised if some television stations get rid of their meteorologists and take advantage of this new weather forecasting idea.

“Now your electronic weather forecast, from our Statistical Analysis Weather Machine.”

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