Overview:

What is WestWide Drought Tracker?

The western United States consists of complex terrain where local precipitation and temperature can vary dramatically across short distances, which in turn impact local drought conditions.  The goal of WestWide Drought Tracker (WWDT) is to provide easy access to fine-scale drought monitoring and climate products that can be utilized by a variety of users.  The climate data sets, drought indices, and maps that are found on WWDT use monthly data which are updated with new values at the beginning of each month.

For days 1-10 of each month the NLDAS-2 data are used to provide an initial view of the spatial patterns before the PRISM data are available.  The 1/8th degree (approximately 12 km) NLDAS-2 temperature and precipitation data  are bilinearly interpolated to the PRISM grid and bias corrected by accounting for monthly differences in climatology of NLDAS and PRISM over a common time period from 1979-2011 (Abatzoglou, 2011).  The PRISM data is then assimilated back into the WWDT once it is made available (after day 10 of each month). 

What products are available on WWDT?

Data Sources:

PRISM:

The PRISM Climate Mapping Program (Oregon State University) is an ongoing effort to produce and disseminate the most detailed, highest-quality spatial climate datasets currently available (Daly et al., 1994). PRISM (Parameter-elevation Regressions on Independent Slopes Model) is an analytical tool that uses point data, a digital elevation model, and other spatial data sets to generate fine scale (4-km, 2.5 arc-minutes) grid-based estimates of monthly precipitation and temperature from 1895-present. PRISM uses point measurements of climate data and a digital elevation model of terrain and is constantly updated to map climate in the most difficult situations, including high mountains, rain shadows, temperature inversions, coastal regions, and associated complex meso-scale climate processes. Monthly updates of PRISM data have a latency of approximately 2 weeks and are assimilated into the WWDT by the middle of each month.
http://www.prism.oregonstate.edu/

NLDAS:

The North American Land Data Assimilation System Phase 2 (NLDAS-2; Mitchell et al., 2004) is an ongoing collaborative effort to produce quality-controlled and spatially and temporally consistent land-surface model (LSM) data sets.  The collaborating groups include NOAA (National Oceanic and Atmospheric Administration)/NECP’s (National Center for Environmental Prediction) Environmental Modeling Center, NASA’s GSFC (Goddard Space Flight Center), Princeton University, the University of Washington, the NOAA/NWS Office of Hydrologic Development, and the NOAA/NCEP Climate Prediction Center.  NLDAS uses surface observations of precipitation and surface meteorology reanalysis data from the North American Regional Reanalysis to drive a group of LSMs and produce fine scale (12-km, 1/8 degree resolution) gridded estimates at hourly temporal resolution of temperature, precipitation, wind speed, surface downward longwave radiation, specific humidity, and surface pressure.  For compatibility between NLDAS and PRISM, temperature and precipitation from NLDAS are bilinearly interpolated to the PRISM grid and bias corrected by accounting for monthly differences in climatology of NLDAS and PRISM over a common time period from 1979-2011. The hybridized NLDAS-PRISM dataset (i.e., Abatzoglou, 2011) provides a “first look” of the previous month’s climate and drought prior to the completion of PRISM.
http://ldas.gsfc.nasa.gov/nldas/NLDASnews.php

Drought Indices:

Palmer Drought Indices:
Palmer drought indices are based on a simplified water budget that considers water supply (precipitation), demand (evapotranspiration) and loss (runoff). Input data for these budget terms consist solely of monthly temperature and precipitation. Fixed soil characteristics are supplied independently by incorporating the available water holding capacity of the top 250 cm of the soil acquired from the State Soil Geographic Data Base (STATSGO). Snow and its effects are not represented.

PDSI (Palmer Drought Severity Index) uses temperature and precipitation data to determine the accumulated water excess or deficit (Palmer, 1965). Values are referenced to the local climate so that PDSI in different climates can be more readily compared. The value of PDSI for a particular month is obtained from a weight applied to the previous PDSI and an additional contribution from the present month's Z-index (below).
http://drought.unl.edu/Planning/Monitoring/ComparisonofIndicesIntro/PDSI.aspx

sc-PDSI (self-calibrated PDSI) is a locally calibrated version of the PDSI designed to make values of the PDSI more comparable across space (Wells et al., 2004). The sc-PDSI thus overcomes some of the limitations of the PDSI which were originally calibrated using a few locations in the Midwestern USA. The sc-PDSI ensures that values exceeding +4 occur 2 percent of the time, and likewise for values less than -4.

Palmer Z-Index - The Palmer Z Index measures short-term drought on a monthly scale. The Z-value is also referenced to the specific location and the climate for that time of year.

Standardized Precipitation Index: The SPI (McKee et al., 1993) utilizes only monthly precipitation in its calculation and was developed for two main reasons. First, water supply in the form of precipitation is usually the most dominant component of the soil's water budget, and strongly influences the Palmer calculations. Secondly, a given location can be simultaneously in short-term deficit, medium-term excess and long-term deficit. All forms of the SPI pertain to some explicit time scale, which is part of the name (e.g., 1-month SPI, 24-month SPI). The SPI is "standardized" so that all precipitation climate regimes are represented in common terms that can be compared with SPI from all other locations, based on the historical probability distribution. Monthly precipitation values are first added over the past N (N = 1, 2, 3...72) months. These are formed into a historical series, from which a histogram or empirical distribution is formed. This is then approximated by any of several possible theoretical distributions (the incomplete beta distribution is used here). Various points on the distribution (5th, 25th, 50th, 75th, 95th percentile, etc.) are then mapped onto a normal (bell-shaped curve) distribution. The SPI is then taken as the numerical value on a normal distribution of the departure from a long term average. SPI has no units, and values can be thought of as "like" standard deviations. Values exceeding +2 or -2 are seen only about 5 percent of the time.
http://drought.unl.edu/Planning/Monitoring/ComparisonofIndicesIntro/SPI.aspx

Standardized Precipitation Evapotranspiration Index:
The SPEI utilizes monthly precipitation and average monthly temperature in its calculation and was developed to help overcome some limitations of the SPI (Vicente-Serrano et al., 2010).  With the temperature input potential evapotranspiration (PET) is calculated and a historical time series of the simple water balance (precipitation – PET) is used in place of the precipitation only time series used in the SPI.  The SPEI and SPI are calculated in a similar manner and both indices have the advantage of being multi-scalar. The standardization procedure for SPEI follows the same steps as SPI, however the developers of SPEI recommend using the three parameter log-logistic theoretical distribution to account for common negative values which are found in the time series (precipitation – PET).  
http://sac.csic.es/spei/index.html

References: