What Can This Web Site Do?

The Climate Analyzer creates custom graphs and tables from historical weather station data. These summaries are calculated dynamically, which means that they don't exist until someone requests them. You tell the site which years or months you are interested in and it calculates averages, totals, or other summaries to fit your needs. The results are sent directly to you as a bit stream and are not stored anywhere on a hard drive. We are adding new graphs and tables to the site all the time, but a few examples appear below.

Example Graphs - These are just a few . . . There are many more available.

Monthly precipitation, average daily maximum temperature (Tmax) and average daily minimum temperatures (Tmin) during 2013 at Mammoth Hot Springs.

Departure of 2013 average minimum, average maximum daily temperatures and precipitation from 1981 - 2010 at Mammoth Hot Springs. Missing bars (NA) indicate months in which there were insufficient data to calculate an average or a total.

June average daily maximum and minimum temperatures during 1894 - 2013. Blanks = insufficient data for an accurate average.

Peak Snow pack (water equivalent - inches) for each water year 1969 - 2013 at the Northeast Entrance of Yellowstone National Park.

Daily measurements of snow water equivalent (SWE) and precipitation from Yellowstone's Northeast Entrance 2012-2014. Values are compared to averages from 1971 - 2000 and 1981 - 2010. Data courtesy of the Natural Resource Conservation Service.

Mean Daily Discharge (CFS) at the Yellowstone R. near Corwin Springs 2012 - 2013 compared to the 1920 - 1950 median values. Data courtesy the U.S. Geological Survey.

The Reconnaisance Drought Index, which is a normalized ratio of Potential Evapotranspiration : Precipitation for each water year 1895 - 2013 at Mammoth Hot Springs, Yellowstone National Park. The zero horizontal line is the average for the time period considered. Red bars = years that were drier than average. Blue = years that were wetter than average. N/A = insufficient data for an accurate calculation. Dashes = groups of years with insufficient data.

Where do the data come from?

Our data sources include the National Weather Service (COOP / GHCN data), The Natural Resources Conservation Service (SNOTEL), USGS (stream gages), Remote Automated Weather Stations, the Hydrological Automated Data System, and a variety of other met stations and dataloggers. All of these data update every 24 hours. Even though new data arrives every day, there may sometimes be a lag of a week or two before measurements are available. So the newest data on this web site may be a week or two old for some weather stations.** It is also important to be cautious when you interpret recent data because they are often provisional and subject to change. **In other words, if you are looking at data from just a few weeks ago, expect that some of it may change as the original data providers review it and correct errors. As these corrections become available, they will be incorporated into the data available on this site.

We do not edit or change the "official" data, but we do screen out months or years that have a large number of missing values (see next section below). In addition to this official data, some weather stations on this web site have a second "corrected" data set that can be compared to the official sources. These alternative datasets were produced by National Park Service staff or contractors for use in specific research applications. They have been screened for outliers or other flaws that commonly occur in weather data. In some cases, these alternative datasets contain estimated values for measurements that were missing in the original record. **All of the tables and graphs generated from corrected (edited) data are prominenently labeled "corrected data." There is no danger of mistaking the two types of data sets. If you don't work for or with the National Park Service, we recommend that you use the "official" data sets on this web site instead of the corrected values.

Why are there blanks in some of the graphs and tables?

Many climate data sets have missing values. At manual weather stations, the observer might not have written down an observation for a particular day. At an automated station, there may have been an equipment malfunction or a data processing error. It is important to handle missing values correctly when you calculate monthly or annual total / averages. If you blindly calculate a monthly temperature average for a month that has only 2 days of data, for example, you might get an average value that is not very representative of reality. Therefore, this web site will not display a monthly / annual total or average for a climate parameter if there is "too much" missing data. In this context, "too much" data is missing if there are more than 5 days of temperature or 3 days of precipitation measurements missing during a month. For annual total / averages, a year will be left blank in a graph or table if there are more than 15 days of missing measurements. In the case of Remote Automated Weather Stations (RAWS), which collect data more often than once/day, days which have more than 6 hours of missing data are considered incomplete and therefore "missing." After daily maximum / minimum and total values are calculated for each day, the same criteria used for daily data at the other stations (5 days temperature, 3 days precip per month, etc.) are applied to RAWS data. Because of this screening, data on this site will sometimes have more blanks or 'n/a' (missing) fields than unscreened data available elsewhere. But non-missing data is not edited by this site and will not differ from official dtaa.

Why not just get the data from the original sources?

This web site is a LOT easier to use and does a lot of the analysis work for you.. If you want to download weather data yourself and do your own analysis, then you may be interested in using our Windows Desktop analysis software, which is available as a free download here. This web site also combines data from four different sources (agencies) into one place, which saves you time. More importantly, this web site can analyze data rather than just providing it to you. For example, you can calculate monthly totals / averages and their departure from 30-year averages, annual statistics such as Accumulated Growing Degree Days (AGDD), freeze / frost dates and extreme day counts.

How is the Reconnaissance Drought Index (RDI) on this web site calculated?

The formula for the normalized RDI is the ratio of precipitation to evapotranspiration divided by the average of this ratio for the time period selected minus one, or ((p/pet)/(avg-p/avg-pet)-1). Evapotranspiration is calculated using the Penman-Montieth equation, with methods described here. When the index is calculated for COOP/GHCN or SNOTEL stations, physical equations are used to approximate the average values of solar radiation and other values needed for Penman-Montieth by taking into account the weather station's latitude and elevation. When solar radiation, relative humidity, and wind speed data are available from a weather station (as in the case of RAWS), these data are used directly in the Penman - Montieth equation. More information on the Reconnaissance is also available in this article. Experiments conducted by the authors of this web site indicate that using actual solar radiation, relative humidity, and wind speed data makes a difference of about 0.05 drought index units (or less) compared to calculations made for the same weather station using the physical approximations. This is not a large difference from a practical standpoint.

Where can I get more information? Who runs this web site?

This web site is maintained by Walking Shadow Ecology. We are located in Gardiner, Montana, which is the north entrance to Yellowstone National Park. Send questions or comments to: Information {at} YellowstoneEcology.com.