An Introduction to the NASA Water Vapor Project Data Set (NVAP) David L. Randel Thomas H. Vonder Haar Mark A. Ringerud Donald L. Reinke Graeme L. Stephens Cynthia L. Combs Thomas J. Greenwald Ian L. Wittmeyer Science and Technology Corporation-METSAT Fort Collins, Colorado Prepared for Headquarters National Aeronautics and Space Administration Washington, D.C. Under Contract NASW-4715 Technical Representative: Dr. James C. Dodge July 1995 Updated: January 1998 TABLE OF CONTENTS 1.0 NEW NVAP DATA AVAILABLE 1.1 AN INTRODUCTION TO NVAP 2.0 INPUT DATA SETS 2.1 RADIOSONDE DATA 2.2 SSM/I DATA 2.3 TOVS DATA 3.0 NVAP PROCEDURES AND DATA DESCRIPTIONS 4.0 QUALITY CONTROL 5.0 HOW TO USE THE NVAP DATA 6.0 REFERENCES APPENDIX A MISSING DATA TABLE APPENDIX B ASCII HEADER FOR CCDA FORMATTED DATA 1.0 MORE NVAP DATA AVAILABLE The NVAP data sets now include 1993, 1994 and 1995 so NVAP data currently extends from 1988 through 1995. Also included are two new data products, one of which has been designated as a new data set. Both of these new data products are derived from the SSM/I data. The new data set that has been created is NVAP_SSMI_CLW_DAILY, which contains data regarding cloud liquid water (CLW) taken daily. The second new data product has been included into the NVAP_SSMI_CLW_MNTHLY data set. The new product added to this data set is monthly precipitable water content (PWC). This data set already contains cloud liquid water (CLW) and liquid water path (LWP) parameters. Because this data set now contains three different parameters, it has been decided to rename the data set from NVAP_SSMI_CLW_MNTHLY to NVAP_SSMI_MNTHLY. 1.1 AN INTRODUCTION TO NVAP There is a well-documented requirement for a comprehensive and accurate global moisture data set to assist many important scientific studies in atmospheric science. Currently, atmospheric water vapor measurements are made from a variety of sources including radiosondes, aircraft and surface observations, and in recent years, by various satellite instruments. Creating a global data set from a single measuring system produces results which are useful and accurate only in specific situations and/or areas. Therefore, an accurate global moisture data set has been derived from a combination of these measurement systems. Under a National Aeronautics and Space Administration (NASA) peer-reviewed contract, STC-METSAT has produced an 8-year total and layered (1988-1995) global water vapor data set. The total column (integrated) water vapor data set is comprised of a combination of radiosonde observations, Television and Infrared Operational Satellite (TIROS) Operational Vertical Sounder (TOVS), and Special Sensor Microwave/Imager (SSM/I) data sets. The global layered water vapor data set is created by slicing the total water vapor data set using layered information that TOVS and radiosonde data provides. As companion data sets for analysis, STC-METSAT also prepared two global (over oceans only), integrated liquid water products (since the best global climate models now contain liquid water as an explicit variable). The liquid water path (LWP) product is the liquid water in any region, averaged during all-sky conditions. The cloud liquid water (CLW) is the liquid water in cloudy-only regions using a specified threshold of the liquid water retrievals. The complete data set (all three sets) has been named NVAP, an acronym for NASA Water Vapor Project. STC-METSAT developed methods to process the data at a daily time scale and 1 X 1 degree spatial resolution. More information can be found in a journal paper about NVAP (Randel et.al. 1996). This work has become an internationally accepted contribution to the World Climate Research Programme (WCRP). This document is intended as a user's manual for the NVAP data set. Section 2.0 contains information about the three input data sets and how they were processed. Section 3.0 gives a description of the NVAP output products and the processing procedure used to create them. Section 4.0 is about the quality control on the data that took place at STC-METSAT. Section 5.0 will tell you how to use this data set. Appendix A talks about missing data along with a table containing information about missing data in the products. Appendix B contains the ASCII header information that is included with each global grid. 2.0 INPUT DATA SETS NVAP uses three independent sources as input to produce a combined water vapor product. The three sources, radiosonde, SSM/I, and TOVS are described in Sections 2.1, 2.2, and 2.3, respectively. 2.1 RADIOSONDE DATA An excellent data set to begin creation of a water vapor product is radiosonde data. It is a data set with a long history and is considered "truth" in the science community. Radiosonde data sets were examined from various sources which required significant processing time and quality control. After NVAP was started, a radiosonde data set was made available by Mr. Bill Elliot (Air Resources Laboratory [ARL]/National Oceanic and Atmospheric Administration [NOAA]). Due to the high quality of this set, it was obtained and used for the entire 8 years of NVAP product. STC-METSAT received the radiosonde ASCII text files from ARL/NOAA via ftp. These files included all the station information at 5 layers. The processing of the radiosonde data involved taking the 5 layers of precipitable water content (PWC) and producing three layers (surface-700 mb, 700-500 mb, 500-300 mb) and the total column PWC. This data was then put into 1 x 1 degree size boxes in a gridded format. The following is a brief overview of Mr. Elliot's data processing and quality control: The original source of this data was the Global Telecommunications System. This system which provides radiosonde data for synoptic charts and forecasting models. NCAR decoded and supplied the data to Mr. Elliot's group. From some 1800 stations that have reported since 1973, approximately 900 have been processed. Since the main objective of this processing was to create a useful climatological data set, the stations chosen required a long history of records. For each station 00Z and 12Z soundings were used. Temperature, dewpoint depression, surface pressure, and geopotential heights were extracted for the mandatory levels (surface, 850, 700, 500, 400, and 300 mb). The soundings were terminated at the 300 mb level since humidity measurements are not reliable past this point. Significant levels (including 1000 and 925 mb) were also saved with temperature and dewpoint depression. From this information, dewpoint, relative humidity, and specific humidity were calculated at each level, with precipitable water, temperature, and dewpoint lapse rate between each layer. The main advantage of using this data set is the extensive quality control performed on it. These were an outgrowth of the many errors discovered in individual soundings when using radiosonde data in climate studies. (Elliot and Gaffen,1991). Each sounding is checked for the number of levels with valid data. Any observations from stations above 700 mb were not included. An individual sounding is kept if the pressure, temperature, and dewpoint at the surface, 850 mb, and 700 mb levels are present and within acceptable limits. Each parameter has a unique set of limits as described below. Surface pressure: (a) Maximum surface pressure is 1060 mb at sea level. Above sea level, the maximum is adjusted using density as a function of station elevation and the hydrostatic equation. (b) Minimum surface pressure equals surface maximum minus 110 mb Dewpoint depression: (a) This must be equal to or greater than zero and be equal to or less than 49 (UNITS). Temperature: (a) For mandatory levels, a climatological monthly station mean is computed with 1973-1991 data. Any temperature outside plus or minus 4 standard deviations from this mean fails the check. (b) For significant levels, temperature was checked against the limits for the surrounding mandatory levels. (c) If a temperature failed a check at a mandatory level 700 mb or below, the entire sounding was discarded. Above 700 mb, the sounding was kept, but data above the last valid level was discarded. This allows level data containing the bulk of water to be retained. Failure at a significant level resulted in that particular level being ignored, but the sounding was retained. Only two adjustments were made to soundings under certain circumstances, both of which were to humidity measurements made under U.S. procedures. When a temperature is below -40 C, the customary U.S. procedure is to discontinue humidity readings (Elliot and Gaffen, 1991). However, Canada reports humidity down to -65C. Taking the median measure from the Canadian data, relative humidity is adjusted to 50% at mandatory levels 500, 400, and 300 mb if temperature is less than -40 and dewpoint is missing. Another U.S. procedure reports relative humidity less than 20% as 19% and dewpoint depression as 30. If 30 was used as the dewpoint depression, the relative humidity would generally be too low. This can lead to a "dry bias". However, using 19% (or treating the data as missing) can lead to a "moist bias". Instead, a mean Canadian value of 16% was used in this situation. Radiosonde is a tested and much used data set but has some noteable problems some of which include geographically sparse stations and soundings which are generally limited to land. There is also an inconsistency in sensors used throughout the world. These topics are discussed in Section 4.0 (Quality Control). 2.2 SSM/I DATA Another source used to create the merged PWC is the SSM/I instrument aboard the F8, F10, F11 and F13 Defense Meteorological Satellite Project (DMSP) satellites. The DMSP F8 satellite (launched June 19, 1987) provided data through 1991. The DMSP F11 satellite (launched November 28, 1991) provided data through 1996. The DMSP F10 satellite was launched December 1, 1990 but had problems with it's orbit which now precesses in a non-synchronous orbit. We were able to calibrate the F10 data for use beginning January, 1993. The DMSP F13 satellite (launched March 24, 1995) began providing data in May 1995. The Wentz routines were used to read and process the raw SSM/I antenna temperatures and produce brightness temperatures at various channels along with the angle of incidence and F8 geolocation error corrections. Also provided was a file containing a list of time periods containing erroneous F8 data. This file helped the quality control of the SSM/I data through the years 1988-1990 but seemed to be incomplete for data through 1992, which contained bad orbital swaths. STC-METSAT used a retrieval scheme based on the physical method employed by Tjemkes et al. (1991). Greenwald et al. (1993) extended and improved this physical method to include the retrieval of liquid water. In order to determine the total column water vapor by using the retrieval model of Greenwald et al. (1993), several input parameters are required. The first parameter is sea surface temperature (SST) (Reynolds, 1988). NVAP used SST's produced by the National Meteorological Center (NMC) on a 2 x 2 degree global grid. We set up a bilinear interpolation routine to use the SST data as a 1 x 1 degree grid. Starting with 1993, the SST data came to us at higher resolution, both temporally and spatially. The new 1 x 1 degree spatial resolution meant we did not have to use the bilinear interpolation on the data. And the SST data are now weekly values versus the monthly data we had before. Other important parameters are the vertical and horizontal components of the SSM/I 19.35 GHz, 22 GHz, and 37 GHz brightness temperatures. These were used in an approximate radiative transfer model. In addition, the near surface wind speed is needed in an empirical sea surface emissivity model (Petty, 1990). To calculate the surface wind speed, the vertical component of the 22 GHz brightness temperature is used in the Goodberlet et al. (1989) surface wind speed algorithm. For SST greater than 300 K, Bates (1991) surface wind speed algorithm is used due to problems which the Goodberlet algorithm has in regions with large water vapor amounts. Starting with the 1993 data, we updated the retrieval algorithm to include iterations using the 22 GHz temperatures along with the 37 and 19.35 GHz channels to find the PWC values, instead of using the 22 GHz channel to only calculate the surface winds. We've also added the ISCCP cloud top temperatures for mid-level clouds instead of approximated cloud top values. We improved the wind calculations, iterative schemes and added the ability to input more than one satellite's data since we now have up to three satellites providing data. Overall, the new PWC (1993-1995) values produce a global average PWC about 0.5 mm less than before. This is due to the better performance in the polar regions, leaving more good data in those low value areas. And it seems to be providing more detailed lower PWC values in the mid-latitudes. For the retrieval routine to produce consistent, stable data throughout all 8 years of processing, the F8 and F11 satellite data were specially calibrated during the month they overlapped (December, 1991). Coincident passes were compared and retrieval parameters adjusted accordingly. The F10 was calibrated with F11 to use it beginning 1993. And the F13 was calibrated to the F10 and F11 when we began receiving data for it in May 1995. The retrieval routine returns pixel values for PWC, liquid water, and wind speeds from the satellites' scanlines. Combining and averaging the pixels into 1 x 1 degree boxes produces the SSM/I output grid maps. Mainly, there are two problems with SSM/I data. One of which is with the brightness temperatures being contaminated by land and the other is contamination due to sea ice. SSM/I is currently used only over oceans. In order to do this a land mask is used to avoid calculating retrievals over land. However, some regions of small islands and rock outcroppings are not included in the mask. These cause occasionally high values in specific island areas. The final quality control included searching for these problem areas. The second problem occurs when the sensor encounters sea ice. It will produce values higher than the actual temperature. This can occur around polar coastal regions but does migrate north and south in accordance with the seasons. Employing a sea ice detection routine allowed for the removal of these bad points. Sea ice areas were also explicitly looked for in quality control. We changed the sea ice detection routine starting with the 1993 data. Before, we detected sea ice at the pixel level using a simple version of the AES/YORK algorithm developed for the SSM/I calibration/validation effort (Hollinger et al. 1991). We changed the sea ice detection routine to use a method by Cavalieri et al. (1991). It means ice concentration in a specified area is +15%, roughly corresponding to sea ice edge. We also included a method by Grody (1991) to locate permanent ice. These methods improved our sea ice detection, especially around coastlines. We also added an oceanic precipitation contamination test. We use the Grody (1991) algorithm already in place within the sea ice detection routine. The algorithm, an 85.5 GHz scattering index, returns a surface type classification including oceanic precipitation. 2.3 TOVS DATA Operational satellite-based PWC retrievals have been made since 1978 by NOAA/National Environmental Satellite Data and Information Service (NESDIS) (Werbowtzki 1981), using raw data collected from the NOAA series of operational polar-orbiting satellites. The NOAA satellites have a near-polar sun-synchronous orbit with a 102-minute period. Carried aboard these platforms is the TOVS instrument package for retrieval of atmospheric temperature, ozone, and water content. The TOVS system is made up of the second-generation High Resolution Infrared Radiation Sounder (HIRS/2), the Microwave Sounding Unit (MSU), and the Stratospheric Sounding Unit (SSU). All three instruments are used for retrieval of vertical temperature and moisture profiles. Both the HIRS/2 and the MSU instruments are cross-track scanners, capable of sensing a swath 2250 km wide. The HIRS/2 spectrometer has 19 infrared channels and one visible channel, and are operated simultaneously during each scan (see Wu et al. (1993) for a discussion of related brightness temperature standard errors). Radiance data from HIRS/2 and MSU channels used primarily for temperature sounding are utilized by the operational NESDIS retrieval scheme for determining water vapor content in three layers. A statistical eigenvector regression method was used before 16 September 1988, and was succeeded thereafter by a physical scheme (Reale et al. 1989) where temperature and moisture profiles are generated in a single-solution vector. Three channels in water vapor absorption bands are located at 6.7, 7.3, and 8.3 micrometers with weighting functions peaking at 400, 600, and 900 mb, respectively. STC-METSAT used the operational TOVS sounding produced by NESDIS. This quality controlled radiance data was available on 8-mm tapes from other members of the water vapor science community (e.g. John Bates at NOAA/Cooperative Institute for Research in Environmental Science [CIRES]). These data were not gridded but included total and 3-layered PWC for approximately 25,000 retrievals per day with geographical spacing of approximately 2 degrees. Processing consisted of gridding this data into 1 x 1 degree boxes. The quality control needed was minimal and was done during the merge process when all three data sets were compared. The few points found to be bad were usually in desert or coastline areas. Beginning with 1994, the TOVS data set we were receiving doubled in size. The computer hardware and software available to the collection site was upgraded, allowing them to retrieve much more of the data from the satellite. The coverage is now much more complete, improving the NVAP product, especially over land. There are two problems inherent in all infrared moisture retrievals that tend to limit the dynamic range of the TOVS data. First, the inability to perform retrievals in areas of thick clouds can cause a "dry bias" (Wu et al. 1993). Second, limitations in infrared radiative transfer theory can cause significant overestimation of water vapor in regions of large-scale subsidence (Stephens et al. 1994). For these reasons, SSM/I data are given a higher total column water vapor confidence level than TOVS data. 3.0 NVAP PROCEDURES AND DATA DESCRIPTIONS The three input data sets described in the previous section each have their own limitations. Radiosonde data is typically over land and even then, is still too sparse to examine small scale atmospheric moisture fluctuations. TOVS satellite data cannot be used in cloudy regions. SSM/I data currently is contaminated by land and sea ice. Therefore, STC-METSAT has combined the three sets together to form a merged product. To create the water vapor product (PWC) the input data sets are individually gridded into three 1 x 1 degree global grid maps. The SSM/I grid map is then checked for missing data over the oceans. SSM/I regions are then segmented in to specified sizes which are spatially interpolated. SSM/I is considered more accurate in measuring water vapor than the TOVS instrument, hence the need for a weighting scheme when the three input sets are merged together. To begin each radiosonde point is placed on the grid map. Next, the SSM/I grid and TOVS grid are combined together using a weighting of 10% TOVS and 90% SSM/I for coincident points. These are recorded in a data source code (DSC) map which describes the origin of each point in the merged product using a number scheme (8 to 0 with 8 being the highest confidence level and 0 being the lowest confidence level). With decreasing confidence after the radiosonde points (confidence level 8) are the TOVS/SSM/I combination points (confidence level 7), SSM/I only points (confidence level 6), SSM/I interpolated points combined with TOVS (confidence level 5), SSM/I interpolated points (confidence level 4) and TOVS only points (confidence level 3). The total merged PWC product is not finished until it is checked for missing data. Missing regions smaller than a specified size are spatially interpolated. This data is given a confidence level of 2. The remaining areas missing data are filled in using a temporal 3-day running average. This data is given the lowest confidence (confidence level 1) except for any possible missing data (confidence level 0). Appendix A lists the missing data, and with the filled regions identified in the DSC grid map, interpolated points can be easily removed (if needed). An important part of the NVAP data set was the layered PWC. The vertical definition of water vapor is important to the moisture transfer process. These types of data sets have never been readily available before as a global product. Two of the three input data sets contain layered information. This information is used to create the layered PWC. The TOVS data was received in three layers: surface to 700 mb, 700-500 mb, and 500-300 mb. The data was then gridded into a 1 x 1 degree grid box for each of the three layers. The radiosonde data came with more than three layers of information. These layers were added to the radiosonde to create three matching layers for the TOVS data. At this time some basic assumptions on the global PWC distribution were made in order to progress with the layered processing. Layered information from both TOVS and radiosondes were used to 'slice' the total merged PWC product. The reasoning is while the TOVS total PWC may not be as accurate as SSM/I, the fraction of total column PWC in each layer is relatively accurate. While the total PWC may change rapidly in space and time the fraction of the total PWC in each layer changes much more slowly. The variability in the percent-of-the total (POT) is a strong function of latitude and season and does not vary spatially as quickly as the PWC. These two assumptions led to the creation of three global grids (for each day) of POT of the PWC (one for each of the three layers using a combination of radiosonde and TOVS retrievals). These POT grids are then spatially interpolated to cover small missing areas. A temporal 5-day running average is used to fill in larger gaps. A 5-day average can be used (versus a 3-day average in the total merged PWC) because of the slower time variability of the POT of the PWC. A Data Source Code (DSC) map is provided for each of the daily layered POT grids (5 to 0, with 5 being the highest confidence and 0 being the lowest confidence). In order of highest to lowest confidence: radiosonde only is the highest (confidence level 5); coincident TOVS/Radiosonde points are combined together using a weighted 10% TOVS and 90% Radiosonde (confidence level 4); TOVS only points (confidence level 3); spatially interpolated (confidence level 2); and temporally filled data has the confidence level 1. Remaining missing data is given the lowest confidence level (confidence level 0). There are increasing areas of missing data with the upper layers, especially in the polar regions due to lack of moisture. To create the final layered PWC, the total merged PWC grids produced earlier are multiplied by the POT grids. This gives our layered product the advantage of having SSM/I information in it along with TOVS and radiosonde data. Results of the layered PWC product shows that in oceanic areas, roughly 75-85% of the total PWC is in the lowest layer. For elevated terrain, roughly 50% of the PWC is in this layer. In some locations, the surface may even be above 700 mb, such as over the Tibetan highlands, in which case the POT for this layer is zero. Included as a companion data set for analysis (since many GCMs are now beginning to include liquid water as an explicit variable), STC-METSAT also processed the oceanic cloud liquid water path (LWP) on a daily 1 x 1 degree grid from the SSM/I processing. The LWP product is the liquid water in any region, cloud or no cloud, and is based upon the physically based method of Greenwald et al. (1993), but at this time covers ocean areas only. Also produced were the daily grids of cloud liquid water content (CLW) which is the liquid water in cloudy-only regions determined using a threshold of liquid water along with PWC and wind speed requirements. 4.0 QUALITY CONTROL In order to produce a quality product, quality control is essential. An important aspect with this data set was the human interaction. Every daily image has been viewed by various meteorologists which helps to insure a high level of quality in our product. With each input data set, there are certain conditions for which the water vapor calculation may be inaccurate. Knowledge of these conditions enabled the NVAP Quality Control Team to focus on possible problem areas and recognize possible false values. Other factors such as climate, terrain, and time consistency were also taken into account. Two main problems with SSM/I measurements are land and sea ice contamination with sea ice being the most common. Either will cause false high values, making detection fairly easy in most cases. While a seasonal brightness temperature threshold is used to take out the more consistent sea ice contaminated areas, some still remain. Most high latitude seas next to land areas tend to have this problem, including waters adjacent to Antarctica, Greenland, Siberia, and Japan. Often such contamination will appear as a string of high values along shorelines. Land contamination is a minor problem compared to sea ice since land does not move as much and is easier to mask. However, areas containing many small islands or rock outcroppings can occasional contaminate the signal. One such area is around the South Georgia and South Sandwich Islands, southeast of Argentina. Usually, land contamination will appear as a single, high value. Another occasional problem for SSM/I are bad orbital swaths. In the first three years of this project (1988-1990), these were well documented and taken out during processing. However, in 1991 through 1995, the documentation appeared to be incomplete. Thus a number of undocumented bad swaths slipped through normal processing. These are usually easy to recognize upon visual inspection, due to their banana shapes and markedly different PWC values from surrounding areas. Use of the Elliot's radiosonde data set greatly reduced the amount of manual quality control required. Most of the unusual or outrageous values had been eliminated. However, there were still a few trouble spots. These would appear as stations that were consistently higher than surrounding radiosonde and satellite measurements. One example is an island station off the coast of Chile. This station tends to be a single, higher value compared with nearby TOVS, SSM/I, and other radiosonde. It was often removed. Other stations checked for this problem included a few Indian and Asian stations. One possible reason for such a bias is the inherent limitations of various radiosonde instrumentation. There are about a dozen different suppliers of radiosondes world-wide, with wide differences between humidity sensors. The Finnish Vaisala, with a thin film capacitive humidity sensor and the U.S. VIZ, with a carbon hydristor, are considered to have good response times. These instruments are used in the U.S. and European countries and their associates. However, some models have a much slower response time in humidity measurements, especially at upper levels. This lag can cause a moist bias in the readings. India uses a lithium chloride element in their humidity sensors which is known to have a much slower response time than the U.S. or Finnish devices. Another humidity device called "a goldbeater's skin hydrometer" is also known to have a slower response time and it is used by many Asian stations. The slower response of these instruments provides a reasonable explanation for most of the higher values and were removed in these areas. The TOVS data used as input was previously quality controlled. Thus, manual quality control on the individual data set was minimal. However, a few TOVS points needed to be removed after comparisons with surrounding radiosonde and SSM/I data. Of the few points removed, the majority tended to be in dry or desert regions. These areas included central Australia, Namibia, Western Sahara, the coast of Peru, Kazkh (old USSR), and the Middle East. Such areas are documented to have a "moist bias". Overall, TOVS has a "dry bias" due to the lack of using TOVS in cloudy regions (Wu et al., 1993). This is caused by the infrared sensor not measuring the entire column's moisture in cloudy regions. The procedure for manual quality control involved several steps. For all SSM/I data sets, each individual daily grid was visually inspected. Known problem areas as mentioned above were specifically checked. Any other value that did not seem to belong, either visually, climatologically, or in a time series (day before/day after), was checked in the data grid. If a value was suspiciously higher than the surrounding values (for example, a value of 12 mm in a field of 2 mm points), it was removed from the set. Any values that were questionable were marked for later comparison within the total merge product. After the three input sets were merged together, each daily merged product was visually inspected under the same guidelines as before. Suspect values were noted, then traced to one of the three original data set. Comparisons between the suspicious value and the surrounding values in all three input sets were made. If that value was well above any of the surrounding values in all three sets, the value was removed from the original data set. If the value was close to any of the surrounding values, or if the feature was in at least two of the data sets, it was left in. Once complete, the modified input files were remerged into the final product. The individual daily product for each of the three merged layers were visually inspected. Since most of the bad individual points were removed in the previous steps, the main thrust of this inspection was to look for anything out of the ordinary. Very few problems were found. These few were usually found in interpolated areas that contained missing data over an extended period of time. Additional tests were added to the gridding code to assist the manual quality control beginning with the 1993 data. One was a statistical test which checks the standard deviation of the pixel values within a grid box and throws out the extreme pixel values making for a better averaged grid box value. A second test we added is a spatial test for a grid box when it contains less than 10 pixel values. A normal grid box will contain from 50-150 pixel values which are averaged to get the grid box value. When that grid box has very few pixel values, it has a greater chance of not being consistent with the adjacent grid boxes. 5.0 HOW TO USE THE NVAP DATA The NVAP data set is made up of packed integer*2 (16 bit) records after an ASCII header of 144 bytes (8 bits). Each file can hold many global, 1 degree x 1 degree grids (360 x 180). Coordinate (1,1) is at the North Pole and 0 degrees longitude. There are three main products: PWC, LWP, and layered PWC. In addition, supplemental products such as SSM/I PWC, radiosonde PWC, TOVS PWC, cloud liquid water (CLW) monthly averages for 1988-1995 and CLW daily averages for 1993-1995, and DSC for PWC and the layered PWC are included. These products are available in four possible file types, depending on the time period covered (i.e. daily, monthly, pentad, and annual). A daily grid is a grid of a given product for a given day. A full month's worth of daily grids will be contained in one file (e.g. 28 grids for February 1989). A pentad is a five day average of a given product. A year of these averages are contained in one pentad file. A monthly grid is an average of the daily grids for a given month. Twelve monthly averages for a given year will be in one file. An annual grid is the yearly average of the daily grids for a given product. The NVAP data set is listed as eight distinct data sets. This allows users to order subsets of the entire data set, based on the data product. An asterix (*) indicates that a new data set has been created, a double asterix (**) indicates that a new parameter has been included in a pre-existing data set, and a triple asterix (***) indicates the renamed data set. The data set names are: NASA WATER VAPOR PROJECT TOVS PRECIPITABLE WATER DAILY GRID NASA WATER VAPOR PROJECT RADIOSONDE PRECIPITABLE WATER DAILY NASA WATER VAPOR PROJECT SSM/I PRECIPITABLE WATER DAILY GRID NASA WATER VAPOR PROJECT SSM/I LIQUID WATER PATH DAILY GRID NASA WATER VAPOR PROJECT PRECIPITABLE WATER MERGED PENTAD/DAILY NASA WATER VAPOR PROJECT SSM/I MONTHLY GRID NASA WATER VAPOR PROJECT PRECIPITABLE WATER MERGED MONTHLY GRID NASA WATER VAPOR PROJECT SSM/I CLOUD LIQUID WATER DAILY GRID The ***NASA WATER VAPOR PROJECT SSM/I MONTHLY GRID is the new name of the former NASA WATER VAPOR PROJECT SSM/I CLOUD LIQUID WATER MONTHLY GRID data set. It contains three types of files for each month: cloud liquid water (CLW), liquid water path (LWP) and perciptable water content (**PWC). CLW and LWP contain data spanning the full eight years of the NVAP project. **PWC is a new parameter that has been added to the data set with data spanning January 1993 to December 1995. The NASA WATER VAPOR PROJECT PRECIPITABLE WATER MERGED MONTHLY GRID data set contains four types of files: total precipitable water and layered precipitable water, each in monthly and yearly grids. The NASA WATER VAPOR PROJECT PRECIPITABLE WATER MERGED PENTAD/DAILY data set also contains four types of files: total precipitable water and layered precipitable water, each in daily and pentad files. The *NASA WATER VAPOR PROJECT SSM/I CLOUD LIQUID WATER DAILY GRID data set is new. It contains cloud liquid water (CLW) files on a daily basis. This data set spans January 1993 to December 1995. The table below lists which file types are available for each product. Each letter corresponds to the file name format listed under the table. Gridded Product Daily Monthly Pentad Annual Blended Total PWC A G J K Blended l1 PWC AA G JJ KK Blended l2 PWC AA G JJ KK Blended l3 PWC AA G JJ KK Data Source for Total PWC B Layer 1 (l1) Data Source BB Layer 2 (l2) Data Source BB Layer 3 (l3) Data Source BB SSM/I LWP C H SSM/I CLW L I SSM/I PWC D M Radiosonde PWC E TOVS PWC F File name format: A: nvap_YYMM.std I: nvapssmi_YYma.clw B: nvap_YYMM.dsc J: nvap_YYpent.std C: nvapssmi_YYMM.lwp K: nvap_19YY.std D: nvapssmi_YYMM.std L: nvapssmi_YYMM.lwc E: nvaprawn_YYMM.std M: nvapssmi_YYma.std F: nvaptovs_YYMM.std AA: nvap_YYMMl#.std G: nvap_YYma.std BB: nvap_YYMMl#.dsc H: nvapssmi_YYma.lwp GG: nvap_YYmal#.std JJ: nvap_YYpentl#.std KK: nvap_19YYl#.std Key: YY = year (e.g. 89) MM = month (e.g. 01) # = layer number (e.g. 1-3) ma = monthly average .std = PWC .clw = cloud liquid water .lwp = liquid water path Additional information: 1) The layered product divides the PWC into three layers. These layers are: L1 = surface to 700 mb L2 = 700 mb to 500 mb L3 = 500 mb to 300 mb. 2) Data source code files for the total PWC grids are identical in size to the corresponding data grid. It shows how each grid point is derived. Data in this file are integers from 0-8, having the following definitions: 0 = Missing data 1 = Time interpolated-filled 2 = Space interpolated-filled 3 = TOVS only 4 = SSM/I interpolated 5 = SSM/I interpolated / TOVS combination 6 = SSM/I only 7 = TOVS and SSM/I combination 8 = Radiosonde data only The data is ordered by increasing confidence. The data source code files for the layered PWC grids are also identical in size to the corresponding data grid. Data in these files are integers from 0-5, having the following definitions: 0 = Missing data 1 = Time interpolated-filled 2 = Space interpolated-filled 3 = TOVS only 4 = TOVS and Radiosonde combination 5 = Radiosonde data only 3) In addition, there are FORTRAN routines provided to read the data within the user's own program. These have been tested on HP-UX, VAX_VMS, SGI, PCs running NT, and IBM systems. These routines include a simple test global average routine which calls the read subroutine. This only averages all valid points with no geographical area weighting. Also included is a simple printout routine which creates an ASCII listing file. The VMS switch was tested on a VAX 4000/90 running open VMS 6.0. The HP switch was tested on a HP 9000/735 running HP-UX 9.05. The SGI switch was tested on a SGI running IRIX 5.2. 4) HEADER.TXT file, Appendix B, which describes the ASCII header on the NVAP files. These values are filled into the common block used to pass the header and grid point data. 6.0 REFERENCES Bates, J.J., 1991: High-frequency variability of Special Sensor Microwave/Imager derived wind speed and moisture during an intraseasonal oscillation. J. Geophys. Rev., 96, 3411-3423. Cavalieri, D.J., Crawford,J.P., Drinkwater,M.R.,Eppler,D.T.,Farmer,L.D., Jentz,R.R, 1991: Aircraft, active and passive microwave validation of sea ice concentration from the Defense Meteorological Satellite Program (SSM/I). J. Geophys. Res., 96, C12, 21,989-22,008. Elliot, W.P. and D.J. Gaffen, 1991: On the utility of radiosonde humidity archives for climate studies. Bull. Amer. Meteor. Soc., 72, 1507-1520. Goodberlet, M.A., C.T. Swift, and J.C. Wilkerson, 1989: Remote sensing of ocean surface winds with the Special Sensor Microwave/Imager. J. Geophys. Res., 94, 14,547-14,555. Greenwald, T.J., G.L. Stephens, T.H. Vonder Haar, and D.L. Jackson, 1993: A physical retrieval of cloud liquid water over the global oceans using SSM/I observations. J. Geophys. Res., 98, 18471-18488. Grody,N.C., 1991: Classification of snow cover and precipitation using the Special Sensor Microwave/Imager (SSM/I). J. Geophys. Res., 96 D4, 7423-7435. Hollinger, J.P. and DMSP Cal-Val Team, 1991: DMSP Special Sensor Microwave/ Imager calibration/ validation. Naval Research Laboratory, Washington D.C. Final Report, Volume 2. Petty, G. W., 1990: On the response of the Special Sensor Microwave/Imager to the marine environment - Implications for atmospheric parameter retrievals. Ph.D. dissertation, 291 pp., Univ. of Washington, Seattle. Randel, D.L., T.H. Vonder Haar, M.A. Ringerud, G.L. Stephens, T.J. Greenwald, and C.L. Combs, 1996: A new global water vapor dataset. Bull. Amer. Meteor. Soc., 77, 1233-1246. Reale, A.L., M.D. Goldberg, and J.M. Daniels, 1989: Operational TOVS soundings using a physical approach. IGARRS '89 12th Canadian Symposium on Remote Sensing, Vancouver, B.C., 2653-2657. Reynolds, R.W., 1988: A real-time global sea surface temperature analysis. J. Climate, 1, 75-86. Stephens, G.L., D.L. Jackson, and J.J. Bates, 1994: A comparison of SSM/I and TOVS column water vapor data over the global oceans. Meteor. Atmos. Phys., 54, 183-201. Tjemkes, S.A., and G.L. Stephens, D.L. Jackson, 1991: Space borne observations of precipitable water: Part I: SSM/I observations and algorithm. J. Geophys. Res., 96, 10941-10954. Werbowtzki, A., 1981: Atmospheric sounding users guide. NOAA Tech. Report NESS 83, U.S. Dept. of Commerce, Washington, DC, 82 pp. Wu, X., J. J. Bates, and S.J.S. Khalsa, 1993: A climatology of the water vapor band brightness temperatures from NOAA operational satellites. J. Climate, 6, 1282-1300. APPENDIX A MISSING DATA TABLE This appendix is a listing of days with significant missing data from the individual input sets. Missing input data can and does have a great effect on the final merged products. The radiosonde data set was quite reliable throughout the 8-year period. The two satellite inputs each frequently missed data due to various reasons such as data ingest processing, tape recorder problems on-board the satellites, etc. In the individual products, only SSM/I data was spatially interpolated across a missing region of limited size. Missing TOVS and radiosonde data were left alone. After the merging of the three data sets, remaining missing data areas were again checked. For smaller regions, the merged data was spatially interpolated. For larger areas and any remaining missing data area a temporal, 3-day running average was used to fill in. We understand a temporal fill may be unwanted in time averaged studies so a data source code map is supplied for the daily merged products allowing the user to eliminate interpolated data. The following is a table of missing data for the radiosonde, SSM/I, TOVS and final PWC products. The first three values after the date are the PWC average values in mm for the northern and southern hemisphere and the global grid. These PWC values can be skewed due to the lack of data points. For the SSM/I and TOVS data, the number on the right is the percentage of good (not missing) data. If that value is below 10% (6480), the percentage is replaced with the actual count of good grid points. The total number of available grid points is 64800. The radiosonde data in the last column is the number of good grid points for when they number less than 600 stations. The missing data for the total merged PWC product actually are high values because of the interpolation that has taken place. So the lower percentages are a sign that there was significant missing data in one of the input sets. Note: 1988 SSM/I data starts at hour 1700 on January 13th. Fields before that date contain only TOVS and Radiosonde data. BAD RADIOSONDE: Days that have less than 600 GRID points. DATE N.HEMIS S.HEMIS GLOBAL # GOOD 91/025 13.83 33.80 16.64 514.0 91/351 15.72 25.46 17.73 527.0 91/352 16.24 27.53 18.23 512.0 91/353 16.06 29.99 18.23 511.0 91/354 15.28 28.68 17.40 519.0 91/355 16.41 27.69 18.19 501.0 91/356 17.34 28.09 19.30 526.0 91/358 15.98 30.74 18.52 528.0 91/359 15.36 31.41 18.02 501.0 91/360 15.16 28.32 17.97 506.0 91/361 15.15 29.64 18.01 505.0 91/362 14.65 28.16 17.50 507.0 91/363 14.34 29.71 17.48 542.0 91/364 14.45 30.54 17.51 569.0 91/365 14.07 30.83 17.56 548.0 92/001 15.14 30.83 18.32 483.0 92/002 14.54 29.29 17.42 535.0 92/003 16.00 30.11 19.02 513.0 92/004 15.50 28.90 18.59 514.0 92/005 14.52 28.77 17.32 514.0 92/006 14.17 31.23 17.35 520.0 92/007 14.57 30.51 17.56 516.0 92/276 23.66 23.83 23.67 566.0 1993: no days with less than 600 gridpoints 1994: no days with less than 600 gridpoints 95/108 0.00 0.00 0.00 0.0 95/108 0.00 0.00 0.00 0.0 95/108 0.00 0.00 0.00 0.0 95/108 0.00 0.00 0.00 0.0 BAD SSM/I: Days that have less than 30% coverage. DATE N.HEMIS S.HEMIS GLOBAL % OR # GOOD 88/001 0.00 0.00 0.00 0.0 88/002 0.00 0.00 0.00 0.0 88/003 0.00 0.00 0.00 0.0 88/004 0.00 0.00 0.00 0.0 88/005 0.00 0.00 0.00 0.0 88/006 0.00 0.00 0.00 0.0 88/007 0.00 0.00 0.00 0.0 88/008 0.00 0.00 0.00 0.0 88/009 0.00 0.00 0.00 0.0 88/010 0.00 0.00 0.00 0.0 88/011 0.00 0.00 0.00 0.0 88/012 0.00 0.00 0.00 0.0 88/013 23.57 24.61 24.14 16.0 88/028 25.60 26.63 26.20 29.7 88/127 0.00 0.00 0.00 0.0 88/128 0.00 0.00 0.00 0.0 88/129 0.00 0.00 0.00 0.0 88/130 17.19 0.00 17.19 13.0 88/220 40.15 20.54 28.60 28.6 88/262 40.09 18.87 28.91 18.4 88/267 0.00 0.00 0.00 0.0 88/360 0.00 0.00 0.00 0.0 88/361 0.00 0.00 0.00 0.0 88/362 0.00 0.00 0.00 0.0 88/363 25.76 20.67 22.90 4681.0 89/013 26.01 25.19 25.44 25.1 89/014 25.07 19.34 21.42 1946.0 89/015 26.24 24.91 25.39 19.2 89/157 30.28 21.12 24.32 21.3 89/158 0.00 0.00 0.00 0.0 89/159 25.57 22.90 24.00 3038.0 89/168 31.74 21.40 25.69 29.9 89/201 41.98 18.10 25.60 3312.0 89/202 37.13 19.64 28.02 17.8 89/203 37.00 19.10 25.97 18.8 89/204 0.00 0.00 0.00 0.0 89/205 0.00 0.00 0.00 0.0 89/206 35.83 19.56 26.64 29.0 89/293 33.11 18.61 25.07 14.1 89/295 29.24 21.61 24.31 13.8 89/296 0.00 0.00 0.00 0.0 89/297 31.04 19.24 26.23 12.2 90/225 0.00 0.00 0.00 0.0 90/226 38.33 20.26 28.01 26.9 90/237 0.00 0.00 0.00 0.0 90/238 0.00 0.00 0.00 0.0 90/239 36.05 21.04 27.60 27.1 90/255 34.47 18.44 26.93 10.8 90/284 31.50 20.73 25.42 26.8 90/288 33.04 22.13 27.47 27.3 90/291 32.50 20.63 23.97 12.4 90/292 30.66 20.83 25.11 29.3 90/294 0.00 0.00 0.00 0.0 90/295 0.00 0.00 0.00 0.0 90/296 29.12 22.17 25.03 25.2 90/298 28.08 19.73 22.50 6455.0 90/299 0.00 0.00 0.00 0.0 90/300 0.00 0.00 0.00 0.0 90/301 0.00 0.00 0.00 0.0 90/305 31.63 20.74 24.60 22.9 90/309 30.85 23.77 26.99 24.5 90/315 28.99 23.69 26.20 17.5 90/316 31.12 23.15 26.56 27.3 90/325 29.58 24.94 26.99 27.4 90/339 30.69 22.81 25.90 27.0 90/356 0.00 0.00 0.00 0.0 90/357 0.00 0.00 0.00 0.0 90/358 0.00 0.00 0.00 0.0 90/359 0.00 0.00 0.00 0.0 90/360 0.00 0.00 0.00 0.0 90/361 26.57 26.09 26.31 28.9 91/042 27.19 26.20 26.52 26.5 91/057 25.47 24.98 25.17 20.8 91/068 25.61 26.88 26.40 21.9 91/069 25.05 26.03 25.68 3826.0 91/085 27.86 26.13 26.83 28.1 91/120 28.02 21.89 24.45 15.2 91/162 33.12 21.89 26.51 17.2 91/179 37.10 20.64 27.27 26.8 91/180 36.64 20.46 26.71 27.8 91/195 37.84 19.30 26.19 18.3 91/196 35.96 21.14 27.54 27.8 91/214 36.51 19.08 26.44 25.9 91/218 37.28 20.76 27.03 29.2 91/228 38.14 20.63 28.33 27.0 91/315 29.36 24.01 26.34 20.8 91/316 31.11 23.18 26.27 29.1 91/317 29.19 24.00 26.06 28.6 91/339 30.43 24.26 26.23 22.0 91/353 25.48 31.41 28.24 1827.0 91/354 23.52 24.92 24.37 3070.0 91/355 25.80 25.95 25.89 3206.0 91/356 23.42 24.90 24.32 3073.0 91/357 21.60 23.78 22.90 2986.0 91/358 29.95 25.34 26.82 3308.0 91/359 22.91 21.78 22.23 2949.0 91/360 22.21 22.37 22.28 1963.0 91/361 0.00 0.00 0.00 0.0 91/362 21.75 24.24 23.31 3191.0 91/363 21.04 23.25 22.38 3082.0 91/364 21.40 22.55 22.09 2983.0 91/365 23.52 29.59 26.77 4924.0 92/045 23.01 24.05 23.70 3801.0 92/046 24.72 26.50 25.78 28.6 92/158 32.34 21.78 24.95 6376.0 92/159 48.10 22.60 32.08 2978.0 92/169 33.47 22.40 26.41 21.0 92/170 0.00 0.00 0.00 0.0 92/198 35.39 20.59 26.69 29.3 92/357 27.87 23.81 25.30 23.3 92/365 26.99 26.39 26.58 10.8 93/310 28.98 21.90 24.91 27.5 1994: no bad days with less than 30% coverage 1995: no bad days with less than 30% coverage BAD TOVS DAYS : Days that have less than 12% coverage DATE N.HEMIS S.HEMIS GLOBAL % OR # GOOD 88/001 19.82 26.99 23.53 11.8 88/005 19.50 27.42 23.71 3602.0 88/012 21.33 26.87 24.27 2608.0 88/019 20.14 27.90 24.30 3368.0 88/026 20.91 28.43 24.77 3577.0 88/033 19.85 27.94 23.68 3818.0 88/040 21.32 27.32 24.46 3552.0 88/047 21.16 26.89 24.10 4208.0 88/054 20.85 27.36 24.28 4341.0 88/056 21.41 27.52 24.59 10.7 88/057 22.00 28.02 25.08 5604.0 88/058 21.45 26.49 24.12 5303.0 88/059 21.87 26.04 24.07 6104.0 88/061 19.61 26.94 23.10 3527.0 88/068 21.75 27.41 24.63 3620.0 88/075 21.34 27.72 24.46 4044.0 88/082 21.16 26.06 23.71 4537.0 88/089 0.00 0.00 0.00 0.0 88/090 21.94 27.79 24.76 5033.0 88/096 0.00 0.00 0.00 0.0 88/097 21.71 28.37 25.15 4201.0 88/103 22.64 27.95 25.46 4305.0 88/110 24.10 26.41 25.31 3556.0 88/117 0.00 0.00 0.00 0.0 88/118 24.51 26.11 25.33 10.1 88/124 25.26 26.21 25.74 3958.0 88/131 26.58 25.07 25.80 4579.0 88/138 0.00 0.00 0.00 0.0 88/139 25.71 25.26 25.49 4273.0 88/145 27.45 24.00 25.71 4427.0 88/152 27.87 23.13 25.29 3179.0 88/159 0.00 0.00 0.00 0.0 88/160 0.00 0.00 0.00 0.0 88/161 0.00 0.00 0.00 0.0 88/162 0.00 0.00 0.00 0.0 88/163 0.00 0.00 0.00 0.0 88/164 0.00 0.00 0.00 0.0 88/165 0.00 0.00 0.00 0.0 88/166 0.00 0.00 0.00 0.0 88/167 0.00 0.00 0.00 0.0 88/168 0.00 0.00 0.00 0.0 88/169 0.00 0.00 0.00 0.0 88/170 0.00 0.00 0.00 0.0 88/171 0.00 0.00 0.00 0.0 88/172 0.00 0.00 0.00 0.0 88/173 31.16 22.81 26.93 4728.0 88/180 30.51 21.52 25.82 4209.0 88/187 32.46 21.88 26.84 3619.0 88/188 33.15 21.62 27.56 10.8 88/189 32.32 21.97 27.52 11.2 88/190 32.22 21.40 27.27 11.6 88/194 33.01 22.14 27.58 3408.0 88/198 0.00 0.00 0.00 0.0 88/201 32.66 21.43 26.76 3727.0 88/208 33.00 20.80 27.04 3269.0 88/215 0.00 0.00 0.00 0.0 88/216 0.00 0.00 0.00 0.0 88/217 33.54 21.42 27.55 4179.0 88/222 33.38 20.90 27.33 4424.0 88/229 32.89 20.92 26.55 4282.0 88/236 32.64 21.30 27.12 4157.0 88/243 30.97 21.06 25.99 4021.0 88/250 30.74 21.80 26.25 4500.0 88/257 30.00 21.56 25.70 3725.0 88/264 29.17 23.49 26.33 4657.0 88/271 28.56 23.42 26.02 4380.0 88/278 27.67 23.07 25.34 4707.0 88/285 26.69 23.87 25.34 4291.0 88/292 25.32 23.43 24.42 2877.0 88/293 24.46 23.90 24.20 6109.0 88/294 24.42 24.27 24.35 6472.0 88/295 23.77 23.99 23.87 5512.0 88/296 24.37 25.11 24.70 6083.0 88/297 24.13 24.42 24.26 6201.0 88/298 24.11 24.88 24.46 10.3 88/299 24.08 24.38 24.22 2895.0 88/300 22.94 24.24 23.52 4624.0 88/301 24.07 24.03 24.05 10.4 88/302 23.34 23.65 23.49 10.1 88/303 22.84 24.49 23.60 5653.0 88/304 22.44 24.55 23.40 6260.0 88/305 23.36 24.47 23.87 6383.0 88/306 24.29 24.42 24.35 3156.0 88/307 23.78 23.29 23.56 10.5 88/308 23.11 23.54 23.31 5244.0 88/309 22.91 24.15 23.48 5994.0 88/310 23.08 24.00 23.52 10.2 88/311 22.82 23.89 23.31 10.3 88/312 23.10 24.20 23.61 10.5 88/313 23.67 24.47 24.05 3026.0 88/314 22.83 24.50 23.61 10.3 88/315 22.87 23.72 23.29 6213.0 88/316 22.85 23.83 23.32 10.5 88/317 23.33 24.13 23.69 10.4 88/318 22.00 24.14 22.97 5786.0 88/319 23.35 23.59 23.46 10.3 88/320 23.43 23.36 23.40 3299.0 88/321 22.85 24.20 23.52 10.2 88/322 22.01 24.45 23.18 10.5 88/323 21.57 24.34 22.9 6282.0 88/324 22.41 24.55 23.47 10.1 88/325 21.78 24.31 22.98 10.3 88/326 21.93 25.64 23.66 6118.0 88/327 21.96 25.66 23.62 2488.0 88/328 22.28 25.19 23.65 10.1 88/329 22.85 25.76 24.24 6028.0 88/330 23.00 26.15 24.53 10.1 88/331 23.62 25.84 24.65 10.4 88/332 22.30 26.02 24.01 10.2 88/333 22.34 25.57 23.89 6265.0 88/334 22.96 25.57 24.28 3241.0 88/335 22.41 25.53 23.96 10.2 88/336 22.26 25.51 23.82 10.0 88/337 22.38 25.86 24.03 6296.0 88/338 22.61 25.16 23.85 10.4 88/339 22.52 26.05 24.21 6003.0 88/340 21.98 25.40 23.65 10.3 88/341 21.79 25.91 23.79 5526.0 88/342 22.14 25.64 23.89 10.5 88/343 21.51 25.31 23.42 6441.0 88/344 21.16 26.11 23.60 10.4 88/345 20.72 25.64 23.14 6263.0 88/346 21.13 25.50 23.18 6454.0 88/347 21.03 24.91 22.99 10.6 88/348 21.59 25.45 23.55 6339.0 88/349 21.11 25.27 23.11 10.5 88/350 21.26 24.49 22.76 10.2 88/351 20.88 25.22 23.03 5707.0 88/352 19.92 25.14 22.54 6283.0 88/353 20.38 25.67 23.04 6293.0 88/354 20.24 25.91 23.05 10.0 89/001 0.00 0.00 0.00 0.0 89/002 20.57 26.44 23.31 5860.0 89/008 0.00 0.00 0.00 0.0 89/009 0.00 0.00 0.00 0.0 89/010 0.00 0.00 0.00 0.0 89/011 0.00 0.00 0.00 0.0 89/012 0.00 0.00 0.00 0.0 89/013 0.00 0.00 0.00 0.0 89/014 0.00 0.00 0.00 0.0 89/015 0.00 0.00 0.00 0.0 89/016 0.00 0.00 0.00 0.0 89/017 19.44 26.12 22.80 1103.0 89/029 21.31 27.14 24.28 11.4 89/030 0.00 0.00 0.00 0.0 89/031 0.00 0.00 0.00 0.0 89/032 21.65 27.53 24.66 2423.0 89/078 22.39 27.09 24.74 12.0 89/212 0.00 0.00 0.00 0.0 89/213 0.00 0.00 0.00 0.0 89/214 0.00 0.00 0.00 0.0 89/215 0.00 0.00 0.00 0.0 89/216 0.00 0.00 0.00 0.0 89/217 0.00 0.00 0.00 0.0 89/218 0.00 0.00 0.00 0.0 89/282 23.81 24.50 24.05 803.0 89/348 0.00 0.00 0.00 0.0 90/071 20.55 25.71 23.07 4435.0 90/129 25.67 24.46 25.01 12.0 90/131 26.68 24.65 25.54 11.5 90/133 26.26 25.43 25.82 4335.0 90/134 26.05 25.03 25.50 4892.0 90/136 26.60 26.01 26.27 6199.0 90/137 26.39 25.00 25.66 4592.0 90/153 26.84 22.58 24.57 3788.0 90/161 0.00 46.00 46.00 1.0 90/232 29.79 18.23 24.71 760.0 90/246 0.00 0.00 0.00 0.0 90/247 0.00 0.00 0.00 0.0 90/248 0.00 0.00 0.00 0.0 90/249 0.00 0.00 0.00 0.0 90/250 0.00 0.00 0.00 0.0 90/251 0.00 0.00 0.00 0.0 90/252 0.00 0.00 0.00 0.0 90/253 0.00 0.00 0.00 0.0 90/254 26.42 22.21 24.24 983.0 90/260 28.03 22.39 25.44 3697.0 90/267 25.45 18.96 22.81 1046.0 90/286 25.94 21.92 24.18 4365.0 90/287 0.00 9.00 9.00 1.0 90/323 0.00 0.00 0.00 0.0 90/324 0.00 0.00 0.00 0.0 90/325 0.00 0.00 0.00 0.0 90/326 0.00 0.00 0.00 0.0 90/327 0.00 0.00 0.00 0.0 90/328 0.00 0.00 0.00 0.0 90/329 0.00 0.00 0.00 0.0 90/330 0.00 0.00 0.00 0.0 90/331 0.00 0.00 0.00 0.0 90/332 0.00 0.00 0.00 0.0 90/333 0.00 0.00 0.00 0.0 90/334 0.00 0.00 0.00 0.0 90/335 0.00 0.00 0.00 0.0 90/336 0.00 0.00 0.00 0.0 90/337 0.00 0.00 0.00 0.0 90/338 0.00 0.00 0.00 0.0 90/339 0.00 0.00 0.00 0.0 90/340 0.00 0.00 0.00 0.0 90/341 0.00 0.00 0.00 0.0 90/342 0.00 0.00 0.00 0.0 90/343 11.00 0.00 11.00 1.0 91/028 0.00 0.00 0.00 0.0 91/029 0.00 0.00 0.00 0.0 91/030 0.00 0.00 0.00 0.0 91/031 0.00 0.00 0.00 0.0 91/032 0.00 0.00 0.00 0.0 91/033 0.00 0.00 0.00 0.0 91/034 0.00 0.00 0.00 0.0 91/035 0.00 0.00 0.00 0.0 91/036 17.78 23.98 20.84 3893.0 91/135 25.05 24.48 24.75 11.9 91/189 31.40 20.31 25.27 10.9 91/195 31.42 20.09 25.57 10.9 91/303 22.93 22.70 22.83 1312.0 91/319 23.27 23.32 23.29 5924.0 91/320 22.04 23.85 22.80 4737.0 91/321 22.01 24.87 23.25 4182.0 91/322 22.29 24.59 23.27 10.9 91/337 22.42 24.70 23.50 11.9 91/349 20.69 25.71 23.00 6209.0 91/357 0.00 0.00 0.00 0.0 91/358 0.00 0.00 0.00 0.0 91/359 0.00 0.00 0.00 0.0 91/360 0.00 0.00 0.00 0.0 91/361 0.00 0.00 0.00 0.0 91/362 0.00 0.00 0.00 0.0 91/363 0.00 0.00 0.00 0.0 91/364 0.00 0.00 0.00 0.0 91/365 0.00 0.00 0.00 0.0 92/007 19.24 24.37 21.75 11.2 92/008 18.33 24.49 21.22 5037.0 92/009 18.93 24.68 21.77 5622.0 92/010 19.52 24.90 22.14 5111.0 92/011 19.62 24.58 22.03 5647.0 92/012 18.44 24.84 21.48 4699.0 92/013 18.44 25.70 21.91 5562.0 92/014 19.94 25.76 22.82 10.7 92/077 20.72 25.84 23.41 10.9 92/078 20.13 25.72 23.11 11.7 92/079 20.92 25.39 23.24 6383.0 92/080 20.12 25.23 22.62 11.1 92/081 19.43 25.86 22.49 6478.0 92/082 20.20 25.96 23.01 6040.0 92/180 28.00 21.94 24.80 5579.0 92/181 28.52 21.87 25.12 10.1 92/274 26.09 22.92 24.66 12.0 92/341 21.73 24.50 23.05 11.8 92/343 21.23 24.93 22.99 5772.0 92/344 19.50 24.56 21.98 3053.0 93/001 20.78 25.51 23.16 11.4 93/002 20.53 25.17 22.92 10.6 93/003 21.24 24.58 22.97 11.2 93/032 22.23 25.51 23.96 6078.0 93/033 22.59 24.52 23.58 5512.0 93/303 25.76 25.49 25.63 11.8 94/044 0.00 0.00 0.00 0.0 95/156 0.00 0.00 0.00 0.0 95/157 0.00 0.00 0.00 0.0 95/158 0.00 0.00 0.00 0.0 95/159 0.00 0.00 0.00 0.0 95/160 0.00 0.00 0.00 0.0 95/161 0.00 0.00 0.00 0.0 95/162 0.00 0.00 0.00 0.0 NVAP TOTAL PWC GRIDS WITH MISSING DATA DATE N.HEMIS S.HEMIS GLOBAL % GOOD 88/001 20.22 26.40 23.28 91.0 88/003 19.67 25.86 22.76 99.6 88/004 19.49 26.01 22.75 99.6 88/009 19.68 26.46 23.06 99.8 88/058 21.23 28.48 24.84 99.7 88/115 24.48 27.34 25.88 92.1 88/160 29.97 21.33 25.63 96.3 88/161 30.32 21.20 25.74 96.4 88/162 30.35 20.82 25.57 96.5 88/163 30.83 21.04 25.92 96.5 88/164 30.86 20.80 25.81 96.5 88/165 30.85 20.76 25.78 96.5 88/166 30.96 20.77 25.86 96.3 88/167 30.97 20.50 25.72 96.2 88/168 31.34 20.55 25.92 96.4 88/169 31.41 20.17 25.77 96.5 88/170 31.45 20.31 25.85 96.4 88/171 31.72 20.38 26.02 96.1 88/189 34.01 21.52 27.90 89.6 88/288 27.40 21.95 24.71 92.5 88/351 20.78 25.61 23.19 95.2 88/352 20.61 25.95 23.27 94.6 88/353 20.93 25.77 23.34 95.0 89/003 20.37 26.17 23.27 97.9 89/004 20.43 25.91 23.17 98.3 89/005 19.90 26.16 23.03 98.3 89/006 20.07 26.27 23.16 96.8 89/009 20.10 26.17 23.15 96.4 89/010 19.98 25.83 22.92 96.4 89/011 20.14 25.99 23.08 96.4 89/012 19.83 26.07 22.97 96.3 89/013 19.79 25.86 22.87 95.9 89/014 17.42 24.94 21.28 83.3 89/015 20.02 25.89 22.98 96.3 89/016 19.99 26.33 23.17 97.8 89/031 19.48 26.70 23.08 97.7 89/124 24.79 25.64 25.21 92.4 89/171 30.26 21.18 25.79 92.0 89/213 33.88 18.59 26.22 96.8 89/214 33.74 18.74 26.26 96.4 89/215 34.05 18.96 26.52 96.4 89/216 33.96 18.73 26.35 96.3 89/217 34.25 18.57 26.42 96.3 89/282 27.87 21.10 24.53 92.3 89/302 25.33 23.23 24.30 91.9 89/350 20.46 25.47 22.96 98.0 89/351 20.59 25.31 22.95 98.0 89/352 20.44 25.31 22.87 98.0 89/353 20.33 25.23 22.78 98.2 89/354 20.55 25.56 23.06 97.5 89/355 20.68 25.61 23.14 97.1 90/030 20.05 28.32 24.16 94.6 90/053 19.65 27.38 23.51 99.8 90/247 32.24 18.30 25.21 96.0 90/248 32.22 18.41 25.28 96.0 90/249 32.44 18.48 25.43 96.0 90/250 32.32 18.49 25.39 95.9 90/251 32.33 18.79 25.54 95.8 90/252 31.92 18.78 25.30 96.0 90/253 31.63 19.21 25.32 96.7 90/285 27.35 21.67 24.55 91.6 90/324 25.48 24.99 25.24 74.0 90/325 25.12 25.40 25.26 74.0 90/326 24.97 25.31 25.13 73.4 90/327 25.42 25.61 25.51 73.8 90/328 25.21 25.32 25.26 73.7 90/329 24.95 25.63 25.29 73.9 90/330 25.10 25.83 25.46 73.8 90/331 24.90 25.54 25.22 73.1 90/332 24.79 25.43 25.10 73.2 90/333 25.01 25.58 25.29 73.6 90/334 23.72 23.65 23.68 96.2 90/335 23.64 23.20 23.42 96.1 90/336 23.77 23.50 23.64 95.7 90/337 23.41 23.24 23.32 95.7 90/338 23.10 23.65 23.38 95.6 90/339 22.79 23.64 23.22 94.3 90/340 22.66 23.56 23.12 95.2 90/341 22.69 23.72 23.21 95.7 90/342 22.60 23.44 23.03 95.7 90/357 19.79 24.32 22.06 96.8 90/358 19.77 24.56 22.16 97.4 91/029 19.70 27.24 23.51 95.3 91/030 19.90 27.42 23.70 95.4 91/031 20.15 27.55 23.88 95.3 91/032 20.31 27.19 23.78 95.6 91/033 20.12 27.69 23.93 95.5 91/034 20.10 27.64 23.92 95.0 91/035 20.18 27.90 24.06 99.0 91/309 24.57 22.84 23.71 92.5 91/339 21.54 24.17 22.85 96.7 91/340 22.01 24.36 23.18 97.0 91/346 22.02 25.09 23.55 97.3 91/347 21.79 25.18 23.49 97.8 91/348 21.70 25.26 23.48 97.9 91/349 21.65 25.46 23.55 97.4 91/350 21.40 25.34 23.37 97.1 91/351 21.28 25.61 23.45 97.3 91/352 21.50 25.42 23.46 97.1 91/357 19.52 24.36 21.95 99.5 91/358 14.68 20.99 17.18 59.9 91/359 14.74 20.66 17.07 56.2 91/360 12.84 18.41 14.84 51.6 91/361 13.18 19.97 15.82 55.3 91/362 12.19 20.10 15.36 53.5 91/363 11.91 20.62 15.37 54.7 91/364 14.92 23.10 18.15 61.9 92/140 26.89 23.99 25.46 90.0 92/284 26.94 21.15 24.09 90.1 92/285 26.73 21.14 23.98 90.9 92/322 23.40 23.20 23.30 92.5 92/323 23.10 23.29 23.19 92.4 93/011 19.88 26.27 23.11 92.8 93/012 19.74 26.09 22.95 92.6 93/255 30.27 19.78 25.18 88.5 93/327 23.11 24.14 23.62 91.0 94/363 19.84 26.05 22.95 96.1 94/364 19.79 25.87 22.84 95.7 94/365 19.77 26.01 22.90 95.4 95/157 30.06 20.43 25.27 97.0 95/158 30.27 20.35 25.28 96.2 95/159 30.31 20.20 25.24 96.5 95/160 30.54 19.90 25.20 96.0 95/161 30.74 19.90 25.31 96.1 95/345 20.54 24.31 22.43 94.0 95/346 20.66 24.23 22.45 93.7 95/359 20.11 26.42 23.27 96.5 95/360 20.33 26.26 23.31 95.5 95/361 20.13 26.16 23.15 95.7 APPENDIX B ASCII HEADER FOR CCDA FORMATTED DATA ASCII Header (144 bytes) For NVAP Data (included for each grid) POSITION # BYTES TYPE 1-4 (4) (A) Format code (CCDA = Colorado Climate Data Archive) 5-7 (3) (I) HEADI (1) data source code 8-10 (3) (I) HEADI (2)parameter number or code 11-11 (1) (I) HEADI (3)data type (1=real*4, 2=int*4, 3=int*2, 4=byte) non-VMS (5=real*4,6=int*4, 7=int*2) 12-16 (5) blanks 17-18 (2) (I) HEADI (4) start year 19-21 (3) (I) HEADI (5) start Julian day 22-23 (2) (I) HEADI (6) start hour 24-25 (2) (I) HEADI (7) end year 26-28 (3) (I) HEADI (8) end Julian day 29-30 (2) (I) HEADI (9) end hour 31-34 (4) (I) HEADI (10) xsize number horizontal data points 35-38 (4) (I) HEADI (11) ysize number vertical data points 39-44 (6) (F) HEADR (1) dx horizontal degree spacing (+ for W to E) 45-50 (6) (F) HEADR (2) dy vertical degree spacing (- for N to S) 51-57 (7) (F) HEADR (3) latitude of first data location ( + for NH ) 58-65 (8) (F) HEADR (4) longitude of western most data location 66-76 (11) (E) offset for data (add to data after applying scale) 77-87 (11) (E) scale factor for data (multiply data by this) 88-98 (11) (E) ZINDEF indefinite value 99-104 (6) blanks 105-144 (40) (A) LABEL character label