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Ashrae Climate Data Online3/24/2021
Information includes monthly and annual percentiles, to provide seasonally representative combinations of temperature, humidity, and solar conditions.View a list of station locations with table data in Chapter 14 View a sample station data table.Also available are joint frequency matrices of dry-bulb temperature and time of day (also known as temperature bin data) and additional information, such as time zones of the stations and the months and years that were used for station design condition calculations.
Weather Data Viewer also provides calculators to implement various calculation methods found in the Handbook, such as those for return period minimummaximum temperatures, degree-days to any base, design day temperature, clear sky solar radiation, and load calculation data, as well as a bin data generator. Data and tables have been completely revised for the 2013 edition. An additional Extremely Hot Climate Zone 0 with humid (0A) and dry (0B) zones has been added, and the standard includes climatic data for 5564 locations throughout the world (from ASHRAE Research Project RP-1453, as published in the 2009 ASHRAE Handbook--Fundamentals ). Standard 169 also includes statistical data, such as mean temperatures; daily ranges; degree hours; and seasonal percentages within ranges of temperatures. You can delete the entry if you do not want a custom menu item. The Custom menu appears on the right side of the main menu bar. These single year files do not represent a single year of contiguous measured data but rather are composite years comprising months from different years, selected using statistical criteria (usually the F-S statistic), and modified at the end and beginning of the months to ensure a smooth transition between the non-sequential data. Hourly weather observations, such as those from the NSRDB, represent continuous sequences of measured (or modeled) archived data over a period of record that may contain missing elements. Weather years for energy calculations are derived from such archives. Records consist of hourly measurements or modelled values of typical climatic parameters such as temperature, dew point, direct and diffuse solar radiation (beam and diffuse), and wind speed. These data sets, such as NRELs NSRDB, are generally fully populated with missing or modelled data flagged as such. It is from these data sets that derivative data and data sets are developed, specifically, weather years for energy calculations. Briefly, energy simulations are mainly run to evaluate different scenarios comparing the long-term energy use of the different scenarios, such as different fenestration options or HVAC control strategies. The assumption is that a single year of simulation would represent the typical use over the long-term; 30-years for example. ![]() The years must be fully populated with no missing values so that the simulation tools do not fail upon running. Ideally, the year should represent typical average weather data, and not long-term extremes, but exhibit a range of weather phenomena for the location in question: typical cold conditions, typical hot conditions, yet still giving annual averages that are consistent with the long-term averages for the location in question. So which weather data set to choose Information on selecting weather data is described in a paper by Crawley 1. Trying to find such a year however may be difficult and there is a small chance of finding such a year in a 15 to 20-year data set 3. This was, and is, the approach for generating TRYROW, TMY, TMY2, TMY3, CWEC, WYEC2, and IWEC data. The method was first developed by Hall et al. Sandia method and is now part of an ISO standard (ISO 15927-4) 6. Indices generally include temperature and solar radiation, and (with lower weights) humidity and wind speed. For each calendar month, determine the CDF of the daily means, sorting the values in rank order. For all the years in the data set, calculate CDF of the daily means. Calculate the F-S for each month and select 5 months using a weighted sum of the F-S statistics.Rank the candidate months with respect to the closeness of the month to the long term mean and median. Use persistence criteria to exclude months with the longest run of temperature, the month with the most runs, and the month with zero runs. Concatenate the 12 selected months by smoothing the 6 hours on each side of the transition between months to eliminate discontinuities. Notice that the year is not a year of actual measured data but rather a year comprising of months from possibly twelve different years, smoothed at the edges.
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