The sun is the primary source of earth's weather, by causing differential heating between the tropics and polar regions. This sets up a state of motion in which the atmosphere is always trying to balance itself: the warm air moves poleward in patterns called ridges, and the cooler air moves equatorward in patterns called troughs. In the mid-latitudes (30 to 60 degrees North and South) the rotation of the earth generally causes weather systems to move eastward.
This dynamic process is best seen on the 500-millibar chart. This chart shows the circulation of the atmosphere at roughly 18,000 feet (5486 meters) and is based on soundings taken by weather balloon on a twice-daily basis. These soundings are then plotted on a map and the lines of equal pressure are connected. Ridges extend toward the pole, are usually associated with warm, dry weather, and have the general shape of an upside down "U" in the Northern Hemisphere. Troughs extend toward the equator, are usually associated with cool, wet weather, and have the general shape of a "U" in the Northern Hemisphere. The area of greatest surface instability (thunderstorms) is usually immediately ahead of (to the right of) the 500 mb trough.
Monthly mean temperature maps show the average conditions during a month, but give no information about changes that occurred within the month. A measure of the day-to-day variability of temperature provides some insight into how temperatures changed during the month. Daily temperature variability is highly dependent on the weather systems and air masses that affect a region.
The daily difference in temperature may be lower in areas where a single air mass remains dominant. This can happen under a stable circulation pattern (at the jet stream level) that locks an air mass in place--for example, a strong zonal flow, or a stable ridge/trough pattern. The daily difference in temperature will be higher in areas that experience a greater frequency of frontal passages as cold arctic air moves southward and warmer, maritime air moves northward. This will happen under a variable circulation pattern, or along a stable storm track.
To quantify the variability in daily temperature, the average daily differences in temperature for the current month have been expressed as a ratio of the normal (1961-90) average daily difference. The magnitude of this ratio is expressed by the intensity of the shading on the map. Green shading indicates that daily variability in temperature was less than normal and may be a consequence of a dominant air mass. Red shading indicates that daily temperature variability was greater than normal reflecting a more frequent passage of differing air masses.
The national temperature index expresses temperature departure from the 60-year mean in terms of standard deviations. Each year's value is computed by standardizing the temperature for each of 344 climate divisions in the U.S. by using their 1931-90 mean and standard deviation, then weighting these divisional values by area. These area-weighted values are then normalized over the period of record. Positive values indicate warmer than the mean and negative values indicate cooler than the mean.
The national precipitation index expresses precipitation departure from the 60-year mean in terms of standard deviations. Each year's value is computed by standardizing the annual precipitation in each of 344 climate divisions across the U.S. using the gamma distribution over the 1931-90 period. The gamma statistical distribution takes into account heavy precipitation years and extremely dry years in the historical record (in mathematical parlance, "a zero-bounded skewed distribution"). These gamma-standardized divisional values are then weighted by area and averaged to determine a national standardized value for each year. These national values are normalized over the period of record. Negative values are drier and positive values are wetter than the mean. This index gives a more accurate indication of how precipitation across the country compares to the local normal (60-year average) climate.
The number within the state represents that state’s ranking for the given period, compared with all other such periods for that state for the 109 year period of record. The number 109 equals the warmest or wettest; the number 1 equals the coolest or driest. For example, if a state has number 102, this means that out of 109 years, this stated period ranked 102 out of 109, or eighth warmest or wettest for that state. If a state has number 15, this means that out of 109 years, this stated period ranked 15 out of 109, or fifteenth coolest or driest.
The First Difference Method is an approach developed by Peterson et al.*, to maximize the use of available station records. The First Difference Method involves calculating calendar-month differences in temperature between successive years of station data [FD(yr) = T(yr) - T(yr-1)]. For example, when creating a station's first difference series for mean February temperature, the station's February 1880 temperature is subtracted from the station's February 1881 temperature to create a February 1881 first difference value. First difference values for subsequent years are calculated in the same fashion by subtracting the station's preceding year temperature from the current year temperature for all available years of station data.
Because the USHCN data set has been adjusted to account for biases due to factors such as instrument changes, station relocation, and urban heat island effects, we use the USHCN data when calculating the long-term temperature time series. However the USHCN data set is not updated in near-real-time. For purposes of monitoring most recent changes in climate, we use the near-real-time data from the divisional database and the first difference approach to extend the USHCN time series. Combining the most recent climate division data with the USHCN time series, we calculate a first difference value using the most recent two years of data (e.g., 2001 – 2000) from the divisional database. We then add this first difference value to the last year (e.g., 2000) in the USHCN time series to obtain the current year’s value (e.g., 2001).
* Peterson et al., 1998: 'The First Difference Method: Maximizing Station Density for the Calculation of Long-term Global Temperature Change', Journal of Geophysical Research
The wide variety of disciplines affected by drought, its diverse geographical and temporal distribution, and the many scales drought operates on make it difficult to develop both a definition to describe drought and an index to measure it. Many quantitative measures of drought have been developed in the United States, depending on the discipline affected, the region being considered, and the particular application. Several indices developed by Wayne Palmer, as well as the Standardized Precipitation Index, are useful for describing the many scales of drought.
Common to all types of drought is the fact that they originate from a deficiency of precipitation resulting from an unusual weather pattern. If the weather pattern lasts a short time (say, a few weeks or a couple months), the drought is considered short-term. But if the weather or atmospheric circulation pattern becomes entrenched and the precipitation deficits last for several months to several years, the drought is considered to be a long-term drought. It is possible for a region to experience a long-term circulation pattern that produces drought, and to have short-term changes in this long-term pattern that result in short-term wet spells. Likewise, it is possible for a long-term wet circulation pattern to be interrupted by short-term weather spells that result in short-term drought.
The Palmer Z Index measures short-term drought on a monthly scale. The Palmer Crop Moisture Index (CMI) measures short-term drought on a weekly scale and is used to quantify drought's impacts on agriculture during the growing season.
The Palmer Drought Severity Index (PDSI) (known operationally as the Palmer Drought Index (PDI)) attempts to measure the duration and intensity of the long-term drought-inducing circulation patterns. Long-term drought is cumulative, so the intensity of drought during the current month is dependent on the current weather patterns plus the cumulative patterns of previous months. Since weather patterns can change almost literally overnight from a long-term drought pattern to a long-term wet pattern, the PDSI (PDI) can respond fairly rapidly.
The hydrological impacts of drought (e.g., reservoir levels, groundwater levels, etc.) take longer to develop and it takes longer to recover from them. The Palmer Hydrological Drought Index (PHDI), another long-term drought index, was developed to quantify these hydrological effects. The PHDI responds more slowly to changing conditions than the PDSI (PDI).
While Palmer's indices are water balance indices that consider water supply (precipitation), demand (evapotranspiration) and loss (runoff), the Standardized Precipitation Index (SPI) is a probability index that considers only precipitation. The SPI is an index based on the probability of recording a given amount of precipitation, and the probabilities are standardized so that an index of zero indicates the median precipitation amount (half of the historical precipitation amounts are below the median, and half are above the median). The index is negative for drought, and positive for wet conditions. As the dry or wet conditions become more severe, the index becomes more negative or positive. The SPI is computed by NCDC for several time scales, ranging from one month to 24 months, to capture the various scales of both short-term and long-term drought.