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# Definitions

This page contains a complete list of the irradiance and weather data fields available from SolarAnywhere®. SolarAnywhere generates irradiance data using proprietary models. Remaining data fields are pulled from weather data providers such as NOAA and NASA.

If you’d like to learn about SolarAnywhere weather-data field sources, visit Weather Data Sources

### Global Horizontal Irradiance (GHI)

Global horizontal irradiance is the total solar radiation per unit area measured at a horizontal surface on the earth. It is typically presented in W/m2 and can be broken down into two components: direct normal irradiance (DNI) and diffuse horizontal irradiance (DHI). The relationship between GHI, DHI and DNI is expressed in the equation below:

$GHI=DHI+DNI*cos ⁡(α_{zenith} )$

Units: W/m2

### Direct Normal Irradiance (DNI)

Direct normal irradiance (DNI) is the portion of solar radiation that reaches the earth on a direct path from the sun.

Units: W/m2

### Diffuse Horizontal Irradiance (DIF or DHI)

Diffuse horizontal irradiance (DIF or DHI) is the portion of solar radiation that reaches the earth indirectly. Water vapor, aerosols and clouds reflect and absorb solar radiation, diffusing it throughout the atmosphere.

Units: W/m2

Irradiance calculated prior to taking cloud cover into consideration. Clear-sky irradiance is calculated based on solar geometry, elevation, water vapor, ozone, albedo, aerosol optical depth, wind speed and temperature.

Clear-sky irradiance data is helpful when using solar resource data to analyze PV system performance. It helps in differentiating between cloudy conditions and other circumstances that may be affecting plant performance, such as equipment failure or losses due to snow cover or soiling.

Units: W/m2

### Snow Depth

A measurement of accumulated snow height on the ground. Snow-depth data is helpful in estimating PV system losses due to snow accumulation on modules. Visit Snow Loss Modeling to learn about effective methods for estimating snow losses.

Data is available in all geographic regions. Snow-depth measurements in the contiguous United States are received daily and persisted for every observation until the next measurement is received. Outside the United States, snow depth values are retrieved hourly at a lower spatial resolution. Visit Weather Data Sources for information on SolarAnywhere snow-depth data sources.

Snow conditions are highly variable from year to year. Because of this, we do not include snow depth in requests for typical year data. Typical year files are generated with 100% weighting on the irradiance component. For example, the January data selected for the typical year file might be from January of 2002 because it is the most representative of typical irradiance conditions, but January of 2002 may have experienced lower than average snowfall.

If you only have access to a Typical Year license, but still want to get an idea about average snow depth at a particular site, download the average year summary file when requesting typical year data, or include summary output fields in your API requests.

Units: m

### Solid and Liquid Precipitation

The total solid and liquid precipitation that fell per meter squared. Precipitation data is often used as a key input in PV soiling loss models, such as the HSU model, since rainfall events can remove accumulated dirt, ash, etc., from PV modules.

Data is available in all geographic regions. Precipitation measurements in the contiguous United States are received as daily totals that are then divided by the number of observations included in each day (varies based on requested temporal resolution). Outside the United States, precipitation values are retrieved hourly at a lower spatial resolution. Visit Weather Data Sources for information on SolarAnywhere precipitation data sources.

Precipitation is highly variable from season to season and year to year. Because of this, we do not include precipitation in requests for typical year data. Typical year files are generated with 100% weighting on the irradiance component. For example, the January data selected for the typical year file might be from January of 2002 because it is the most representative of typical irradiance conditions, but January of 2002 may have experienced lower than average rainfall.

If you only have access to a Typical Year license, but still want to get an idea about average precipitation at your site, download the average year summary file when requesting typical year data, or include summary output fields in your API requests.

Units: kg/m2

### Ambient Temperature

Air temperature measured at a height of 2-meters. Ambient temperature is an important consideration in PV performance modeling as temperature affects the performance of PV modules and inverters. PV modules and inverters often demonstrate reduced performance in extreme temperatures.

Data is available in all geographic regions and can be requested as an output field with time-series, typical-year (TGY/TDY/average year) and probability of exceedance data types. Visit Weather Data Sources for information on SolarAnywhere ambient temperature data sources.

Units: Degrees Celsius

### Wind Speed

The wind speed measured at a height of 10-meters. Wind speed is often considered in PV performance modeling since it can have a cooling effect on PV cell temperature. Cooling can improve PV module performance during periods of high ambient temperature.

Data is available in all geographic regions and can be requested as an output field with time-series, typical-year (TGY/TDY/average year) and probability of exceedance data types. Visit Weather Data Sources for information on SolarAnywhere wind speed data sources.

Units: m/s

### Relative Humidity

Relative humidity is a measure of the moisture content of the air at the ambient temperature. Relative humidity is often used in PV performance modeling to estimate the precipitable water content of the air, which can be used in conjunction with air mass to apply spectral correction factors. The spectral composition of the solar irradiance reaching PV modules has been shown to impact system performance.1

Data is available in all geographic regions and can be requested as an output field with time-series, typical-year (TGY/TDY/average year) and probability of exceedance data types. Relative humidity data is not currently available for forecast time periods. Visit Weather Data Sources for information on SolarAnywhere relative humidity data sources.

Units: %

### Albedo

Albedo is a measure of the fraction of the global horizontal irradiance that is reflected off the ground. Visit Albedo Support for a detailed write-up on SolarAnywhere albedo data.

SolarAnywhere albedo data takes snow conditions into consideration. Snow conditions are highly variable from year to year. Because of this, we recommend utilizing the 12 monthly long-term averages presented in the average year summary file for use in PV performance modeling, rather than the albedo data presented in typical year files. Typical year files are generated with 100% weighting on the irradiance component. For example, the January data selected for the typical year file might be from January of 2002 because it is the most representative of typical irradiance conditions, but January of 2002 may have experienced lower than average snowfall which would be reflected in the albedo data.

Visit Average Year Summary to see additional information about this file type and see how to utilize the average albedo values in PV modeling.

Data is available in all geographic regions and can be requested as an output field with time-series, typical-year (TGY/TDY/average year) and probability of exceedance data types. Albedo data is limited to SolarAnywhere versions 3.5 and later and is not currently available for forecast time periods.

Units: Unitless

### Particulate Matter

Particulate matter—also known as particle pollution or PM—is a mixture of small particles and liquid droplets suspended in the air. Particulate matter data has been shown to be highly correlated with soiling losses and has been incorporated by PV simulation models such as the Humboldt State University (HSU) model to estimate soiling losses. Particulate matter may include dust, dirt, soot and smoke, as well as other organic and inorganic compounds. Depending on the size of the particles, particulate matter can be categorized into:

1. Particles with diameter less than or equal to 10 micrometers, known as PM10
2. Particles with diameter less than or equal to 2.5 micrometers, known as PM2.5

Data is available in all geographic regions and can be requested as an output field with time-series, typical-year (TGY/TDY/average year) and probability of exceedance data types. Particulate matter data is limited to SolarAnywhere versions 3.5 and later and is not currently available for forecast or real-time time periods. Visit the particulate matter page to learn more about SolarAnywhere particulate matter.

Units: μg/m3.

### Irradiance Observation Type

The irradiance observation type refers to a two-character code used to indicate two things:

1. The model (historical, real-time or forecast) used in generating the irradiance value
2. Whether the value was observed, missing, estimated or filled with a long-term average

The first character indicates the model used to generate the irradiance value or if the user is not licensed for a portion of the requested date range:

The second character indicates whether the value was observed, missing, estimated, or filled with a long-term average:

Examples:

### Wind Speed and Ambient Temperature Observation Type

SolarAnywhere provides ambient temperature and wind-speed data from global reanalysis models. Periods of missing data can occur due to periods of missing measurements from ground-based sensor networks.

The wind speed and ambient temperature observation types provide a one-letter code indicating if the weather data was observed, missing or filled with long term averages. Users can specify whether they would like SolarAnywhere to fill missing data periods with long-term climatological averages, leave them blank, or fill them with integer or text strings that make them easier to sift out.

Here are the codes that can be provided as wind speed or ambient temperature observation types.

### References

1 Nelson L, Frichtl M, Panchula A. 2013. Changes in Cadmium Telluride Photovoltaic Performance Due to Spectrum. IEEE Journal of Photovoltaics. 3(1):488-493. DOI: 10.1109/JPHOTOV.2012.2226868. Link