This application supports infrastructure developers and engineers by
providing climate design data consistent with modern engineering design
standards. The climate design conditions application will provide actionable
climate information to municipal planners, decision-makers, engineers, and
the wider public.
1. Click on the Design Conditions Statistics button
2. Select a city from the list:
The dropdown list contains a list of cities, each with associated
metadata with their corresponding
World Meteorological Organization(WMO)
identification number. These 5-character alphanumeric codes are
used to access the appropriate data files for the calculations.
3. Select a design standard:
The dropdown list contains two design conditions, ASHRAE
and UFC, which
determine whether the 2009 ASHRAE Handbook format and fields or the 2018
United Facilities Criteria (UFC) are used for the calculations. Both
design conditions result in the creation of two design sheets. The first
uses observed/historical data, while the second employs the CMIP6 model
in conjunction with historical data (projected from the baseline start year to end year to
2050-2070) to compute projected values based on the weather station
chosen.
4. Select a datatype:
The dropdown list currently contains one datatype, observations, which
sources observational data from the NCEI database.
5. Select a start year:
The dropdown list contains a range of start years from 1979 to 2024 for
the period used to calculate the design conditions. 1979 was chosen as
the starting point because it is considered an appropriate point where
data becomes more reliable. Please note that only 30 years can
be calculated at a time. So, for example, if you select 1979 as the start
, the maximum end year you can choose is 2008. Also note that data
selection cannot exceed 2024.
6. Select an end year:
The dropdown list contains a range of end years restricted within a 30-year
range from the start year and maximum of 2024. 2024 was chosen as
the endpoint because it is the last year of complete climate data
available, as 2025 is ongoing.
7. Select an Earth system model:
The dropdown list contains the HadGEM-GC31-MM Earth system model and
CLiMA (ESM),
used to calculate the design conditions. The CLiMA ESM is currently unavailable, so no calculations can be done with it at present.
ESMs simulate the interactions among the Earth's biosphere, lithosphere, atmosphere,
hydrosphere, and cryosphere in the face of external forces, particularly
from the Sun. The ESMs used in this application are sourced from the UK
Met Office Hadley Centre.
8. Click the compute button
Note that the compute button will only activate when all required
options are selected.
The Mean coincident operation is a way to find the
typical value of a target field at a specific value of a source
field. For instance, 'the mean coincident wind speed to 99.6% dry
bulb' tells us the average wind speed that coincides with
temperatures colder than 99.6% of all recorded temperatures over the
period of interest. In this case, it helps understand extreme
conditions.
Humidification involves adding moisture to the air to
make it more comfortable. For instance, when we talk about the
dew-point temperature corresponding to 0.4% and 1.0% annual
cumulative occurrence, we mean a condition in which you might need
to introduce moisture into the air to achieve comfortable indoor
conditions.
The process of reducing moisture in the air to make it more
comfortable is known as dehumidification. For example, when
we talk about the dew-point temperature corresponding to 99.6% and
99.0% annual cumulative occurrence, we're talking about a situation
in which you might need to lower the humidity in the air to achieve
acceptable indoor circumstances.
Enthalpy is a measure of how much energy is in the
air, including the energy needed to change its surroundings. In air
conditioning and HVAC, enthalpy helps us understand the heat in the
air. To calculate enthalpy, we consider the mixing ratio. The mixing
ratio tells us how much water vapor is in the air compared to the
dry air, which is important for understanding air properties. In
short, enthalpy helps us know the air's energy, and the mixing ratio
helps us understand its moisture content.
According to the 2009 ASHRAE Handbook: Fundamentals, the
return period, or recurrence interval, represents the reciprocal of the annual
likelihood of an event happening. For example, a 50-year return
period maximum dry-bulb temperature has a 1/50 chance of occurring
or being surpassed yearly. This statistic doesn't indicate how
frequently this condition will happen in terms of the number of
hours per year (as in percentile-based design conditions). Still, it
characterizes the likelihood of the condition occurring at least
once a year.
The 2009 ASHRAE Handbook: Fundamentals states that
heating and cooling degree days (HDD/CDD), base 50 of
65°F, are calculated as the sum of the differences between the daily
average temperatures and the base temperature. For example, if the
average temperature for a day is 50°F, there are 15 heating degree
days if the baseline temperature is 65°F. These parameters are
useful in energy estimation. That is how much energy we need to heat
or cool a building.
Cooling degree hours, or "CDH," are a measurement of how much (in
degrees) and how long (in hours) the outside air temperature exceeds
a certain "base temperature" (or "balance point"). The baseline
temperatures in the 2009 ASHRAE Handbook: Fundamentals are 74°F and
80°F. Like degree day, they are used to calculate the energy
necessary to cool buildings.
Clear Sky Solar Irradiance is the amount of sunlight
(solar energy) that would reach the Earth's surface under ideal or
clear sky conditions, with no atmospheric interference such as
clouds, dust, or pollutants. Ebn at noon: The sunlight comes
straight from the Sun in a direct line at noon. Edh at noon: The
sunlight (solar energy) comes from all directions at noon, scattered
by the sky and clouds.
The occurrence design value of a weather field is the
value that is exceeded or equaled for a given percentage of the
time. For instance, the occurrence design value of the dry bulb
temperature corresponding to 99.6% cumulative occurrence is the
temperature that is exceeded or equaled for 99.6% of the time.
The median of extreme highs and lows is the median of
the maximum and minimum values for each year in the period of record
for a weather field. For instance, the median of the extreme highs
and lows of the dry bulb temperature is the median of the maximum
and minimum values for each year in the period of record for the dry
bulb temperature.
The mean coincident The mean coincident operation for
the UFC sheet is very similar to the operation used in the ASHRAE
sheet. The calculations differ when the dry bulb and wet bulb
temperatures are used as source fields. The mean coincident value of
a target field when the wet bulb or dry bulb temperature is the
source field is the typical field value when the wet bulb or dry
bulb temperature is within one degree of a specific value. For
example, the mean coincident value of the wind speed when the dry
bulb temperature is 99.6% is the typical wind speed within one
degree of this 99.6% occurrence value.
The air conditioning/ humid area criteriaThis is the number of hours, on average, that the dry bulb
temperature is equal to or exceeds 93°F and 80°F and the wet bulb
temperature is equal to or exceeds 73°F and 67°F.
UFC Design Chart Download Process
When a user selects the UFC Chart Standard from the list of available
standards, the backend executes the following process:
To obtain data for the observations UFC chart, download and store
station data from NCEI
in a local database. Check the quality of the data and fill in all
missing data with the missing data handler, which does so by linear
interpolation and extrapolation. To obtain data for the Projections UFC Chart,
download data from the HadGEM-GC31-MM Earth system model (ESM) and
store it in a local database.
Use the station data to calculate the design values for dry-bulb
temperature, wet-bulb temperature, and humidity ratio.
Next, calculate the mean coincident fields for each of the design
values.
Then, calculate the air conditioning/humid area criteria.
Pass all calculated fields to the UFC chart template and output the
final chart to the user.
Temperature Summary
Climatology Summary
These graphs show observed (lighter color) and projected (darker color)
average dry and dew point temperatures, along with precipitation over time
for a specific location. The projected data is based on the ssp585
scenario from CMIP6, which assesses cumulative emissions and their impact
on climate targets and carbon cycle feedback for the 21st century.
Heating & Cooling Degree Days Graph
Degree Days are a measure of the energy needed to heat or cool a building,
with Heating Degree Days determined by subtracting the average daily
outdoor temperature from a 65°F standard, and Cooling Degree Days
computed by subtracting the average daily outdoor temperature from a
50°F standard. The observed (lighter color) and projected (darker
color) monthly totals are graphically represented.
Temperature Heatmap
The temperature heatmap simultaneously shows the annual and diurnal cycles
for dry bulb temperature in °F. We use typical and future typical year
values to construct the charts in local time at the selected location.
Temperature Yearly Chart
The yearly temperature charts overlay the annual mean dry bulb
temperature, the ASHRAE 80% adaptive comfort, and the dry bulb
temperature's annual mean daily range. We calculate the comfortable
temperature range for 80% of the population using an adaptation of the
ASHRAE-55 Adaptive Comfort Standard, approximated given by:
,where T_{pma(out)}is the prevailing mean outdoor temperature for at
least seven days.
When the prevailing mean outdoor temperature is over 77°F, we assume 1.2
m/s airflow. Therefore, we augment the upper limit by 4°F.
However, these calculations have some limitations. For example, the
adaptive comfort calculations do not hold for outdoor temperatures below
50°F or above 93°F. For more information on the calculation and the
limitations, see
the ASHRAE Thermal Environmental Conditions for Human Occupancy.
Temperature Daily Chart
The daily temperature chart is an amalgamation of monthly temperature
scatterplots, each displaying the temperatures in °F for all hours of
the month. The median temperature for each hour is shown as a solid line.
Wind Summary
Wind Frequency Summary
A stacked bar polar chart is a type of chart that uses a polar coordinate
system to represent data. A succession of bars is organized in a circular
pattern around a center point in the graph. The height of each bar shows
the overall frequency of wind events in that sector, symbolizing a wind
direction or sector.
The bars are divided into segments to represent specific wind speed
ranges. Each segment's length within a bar indicates the percentage of
wind events falling within that speed range. These segments are stacked
atop one another, with the total bar height showing the overall frequency
of wind events in that direction and the length of each segment revealing
the proportion of wind occurrences in each speed range.
By viewing the chart, researchers can immediately get insights into the
prevailing wind directions, the distribution of wind speeds from each
direction, and the overall frequency of wind events in different sectors.
This data could be helpful in a variety of applications, including urban
planning, renewable energy projects (such as wind farms), and even
regional weather forecasts.
Wind Speed Heat Map
The wind speed heatmap simultaneously shows the annual and diurnal cycles
for wind speed in mph. We use typical and future typical year
values to construct the charts in local time at the selected location.
Wind Direction Heat Map
The wind direction heatmap simultaneously shows the annual and diurnal cycles
for wind direction in degrees. We use typical and future typical year
values to construct the charts in local time at the selected location.
Annual Wind Plot
The annual wind plot is a quiverplot that shows the wind speed and direction
for each hour of the year. The color of the arrow represents the wind speed
in mph, and the direction of the arrow represents the wind direction in degrees.
A typical meteorological year summarizes the climate of a region. The
period over which TMY data is calculated varies from 15 to 30 years. If
the period is too short, then the summary is not representative, i.e., it
is not typical. If, on the other hand, the period is too long, then
long-term trends distort the data.
Similarly, a future typical meteorological year (FTMY) summarizes the
projected future climate of a region. The TMY and FTMY year files are
generated in the same way, but the data used to generate them is different.
The TMY uses observational data from the NCEI,
while the FTMY uses projection data calculated for the years 2050–2070.
These projections are derived by adding delta values, calculated from 20-year
EPOCHs and the target years (2050–2070), to baseline years. EPOCHs, which are
arbitrarily chosen averaging periods, help extend projections beyond 1990–2010.
The EPOCHs used are 1970–1989, 1990–2009, and 2010–2029. Consequently, the FTMY is
more accurate when generated for years within an EPOCH range. Baseline
projections are then used to determine typical months, which are projected to
2050–2070. For instance, if a typical month from 1980 is selected for an FTMY
generated for base years 1980–1985, it would be projected to 2060 to align
with the fixed EPOCH range.
TMY data is typically displayed graphically and in tabular form, with
variable temporal resolution, from monthly to hourly resolution.
Actual meteorological year files are comprised of accurate observation
data for a calendar year, placed in TMY file format.
Here, we provide projections of TMY data. The projections are estimates of
the future climate obtained from climate simulations. These simulations
encapsulate our current understanding of the climate system. We use model
output from the UK's HadGEM-GC31-MM global climate model to compute the
projections.
Human influence on the future climate is encapsulated in several emissions
scenarios. We compute our data using the most severe emissions scenario:
SSP585.
Custom Meteorological Year File Download
Select the meterological year file button
Select city from list
Select desired weather file
Select datatype
Select time period
Select desired file type format
Click the download button
Note that the download button will only activate when all the required
fields are filled.
When a user selects the TMY or FTMY option from the list of available
options, the following process is followed:
Obtain the raw data for the specified WMO or WBAN number and period.
For TMY files, source the observation data from the
NCEI database. For
FTMY files, source the data from the HadGEM-GC31-MM Earth system model
(ESM).
Calculate the derived fields such as daily global horizontal radiation
and daily DB, DP, and WS data statistics.
Choose the years to represent each month of the calendar year by using
the Sandia Method. The Sandia Method uses nine daily indices: maximum,
minimum, and mean dry bulb temperature, maximum and mean wind
velocity, and the daily total global horizontal radiation.
Generate a long-term CDF for each daily index over the entire POR each
month. Then, calculate CDFs for each year for that month. Compare each
year's monthly CDF to the long-term CDF by using the
Finkelstein-Schafer (FS) Statistic:
FS = (1/n) \sum_{i=1}^n \delta_i
Where \delta_i is the absolute difference between the long-term
CDF and candidate month CDF and n is the number of daily readings in
the month.
Weigh the FS statistic of each daily index with its respective
weighting, then sum all the weighted FS statistics to obtain the
weighted sum (WS). Select the five candidate months with the lowest
WS.
Rank the candidate months based on their proximity to the global mean
and median.
Test each of the five candidate months against a persistence criteria.
The persistence of the mean dry bulb temperature is done by
calculating the frequency and length of consecutive hot and cold days,
i.e., consecutive days with mean temperatures above the 67th and 33rd
percentile respectively. The persistence of the daily global
horizontal radiation is done by finding the length and frequency of
consecutive low radiation days, i.e, consecutive days with GHI below
the 33rd percentile. Eliminate the candidate months with the longest,
the most frequent, and no runs.
Select the candidate month with the highest rank that passes the
persistence criteria to represent the calendar month in the TMY/FTMY
file.
Concatenate the selected months into a year series for each variable,
and smooth the discontinuities at month boundaries for 6 hours on each
side of the month boundary.
Pass the typical year series to the file-type template that the user
selected. Populate the template, then send the file to the user.
When a user selects the AMY option from the list of available options,
the following process is followed:
Source the observation data used in this process from the
NCEI database.
The missing data handler fills in all missing values using linear
interpolation and extrapolation methods.
Calculate the derived fields, like the humidity ratio.
Use the observational and computed weather fields to build the AMY
file in the appropriate output format for the user.
