Every yield number starts with irradiance data. In India, the same site can show different sun depending on which database you open. Knowing where each dataset comes from tells you which one to trust for which job.
Quick answer. Solar irradiance data for India comes from four public routes: NIWE ground stations, NIWE’s Solar Atlas of India, the Global Solar Atlas, and NASA POWER. NASA POWER shows annual average GHI of about 4.4 to 5.7 kWh/m2/day across ten major cities. Use free data for screening and a vetted commercial or site-measured dataset for bankable yield.
TL;DR
- GHI drives flat and fixed-tilt PV. DNI matters for trackers and concentrating solar. DHI is the sky's diffuse share.
- In NASA POWER data, diffuse light is 35% of GHI in Jodhpur and about 50% in Kolkata and Guwahati.
- Western Rajasthan and Gujarat lead on DNI. The east and northeast trail on both GHI and DNI.
- The monsoon cuts DNI sharply but raises the diffuse share. Look at monthly data, not only annual.
- Cross-check at least two sources before a feasibility decision.
This guide is for developers and EPC engineers running feasibility in India. For how sources compare inside PVsyst, read our meteo data source comparison.
What are GHI, DNI, and DHI?
They are three ways of measuring the same sunlight:
| Component | What it measures | Measured by | Matters most for |
|---|---|---|---|
| GHI | Total sun on a horizontal surface | Pyranometer | Fixed-tilt and rooftop PV |
| DNI | Direct beam on a surface facing the sun | Pyrheliometer on a tracker | Trackers, concentrating solar |
| DHI | Scattered sky light on a horizontal surface | Shaded pyranometer | Cloudy, hazy, and humid sites |
They are linked: GHI equals DHI plus DNI times the cosine of the sun’s zenith angle. A yield model uses all three to estimate plane-of-array irradiance on tilted modules.
Where can you get solar irradiance data for India?
NIWE ground stations (SRRA)
The National Institute of Wind Energy (NIWE), under MNRE, runs the Solar Radiation Resource Assessment (SRRA) network. Each station measures GHI, DNI, and DHI with weather data. Per a NIWE presentation to the BSRN community, stations sample every second and average to one minute.
Ground data is the most direct measurement. Coverage is still sparse, so a station may be far from your site.
Solar Atlas of India (NIWE)
NIWE’s atlas combines satellite and ground data. Per the same NIWE presentation, it used 16 years of satellite-derived data (1999 to 2014) from 3TIER. Data from 54 SRRA stations adjusted the satellite values, and 61 more stations validated them.
Global Solar Atlas (World Bank)
The Global Solar Atlas is published by the World Bank and ESMAP, with data from Solargis. It gives long-term GHI, DNI, DIF (diffuse), and PV output. The World Bank also publishes India GIS layers for download.
NASA POWER
NASA POWER is free, global, and has an open API. It is coarse, at roughly one degree for its solar data. It suits screening and cross-checks, not site-level yield.
Commercial and model datasets
Solargis, Meteonorm, and similar paid datasets are common in lender reports. Solargis is a satellite model. Meteonorm interpolates ground stations and satellite data.
What does the data show across India?
We pulled the NASA POWER climatology (2001 to 2020, API v2.10) on 6 October 2026. Values are long-term daily averages in kWh/m2/day.
| City | GHI | DNI | DHI | Diffuse share (DHI/GHI) | July GHI | March GHI |
|---|---|---|---|---|---|---|
| Jodhpur | 5.66 | 5.37 | 1.98 | 35% | 5.76 | 6.34 |
| Ahmedabad | 5.53 | 4.93 | 2.05 | 37% | 4.48 | 6.56 |
| Bengaluru | 5.48 | 3.72 | 2.40 | 44% | 4.76 | 6.61 |
| Thiruvananthapuram | 5.40 | 3.59 | 2.39 | 44% | 4.94 | 6.40 |
| Hyderabad | 5.36 | 3.89 | 2.27 | 42% | 4.62 | 6.24 |
| Mumbai | 5.29 | 4.04 | 2.15 | 41% | 3.79 | 6.59 |
| Chennai | 5.22 | 3.39 | 2.41 | 46% | 4.87 | 6.35 |
| Delhi | 4.89 | 3.68 | 2.20 | 45% | 5.01 | 5.65 |
| Kolkata | 4.57 | 2.90 | 2.33 | 51% | 4.20 | 5.41 |
| Guwahati | 4.39 | 3.28 | 2.19 | 50% | 4.45 | 4.96 |
Multiply a daily value by 365 for a rough annual total. Jodhpur’s 5.66 is about 2,070 kWh/m2 a year.
Three things the table shows
- GHI varies less than DNI. GHI spans about 1.3 times from lowest to highest city. DNI spans about 1.9 times. Tracker gains vary more by region than fixed-tilt yield.
- The diffuse share is high almost everywhere. Humidity, haze, and monsoon cloud scatter light. Even sunny Bengaluru gets 44% of its GHI as diffuse.
- The monsoon hits the west coast hardest. Mumbai’s July GHI is 3.79, against 6.59 in March. Jodhpur’s July GHI stays close to its annual average, because western Rajasthan sees less monsoon cloud than the west coast.
These are 1-degree cells. Coastal and hill cities can differ from the cell average. Treat the table as a regional picture, not a site value.
Why does the source choice matter?
Different sources can disagree on the same site by several percent. That gap flows straight into energy yield and P90. A 3% GHI difference is roughly a 3% difference in yield.
Satellite models in India must handle dust, aerosols, and monsoon cloud. Those are hard to model, which is why ground validation matters.
Which source for which job?
| Job | Suitable source | Why |
|---|---|---|
| First screening of many sites | NASA POWER, Global Solar Atlas | Free and fast |
| Feasibility for one site | Global Solar Atlas plus one more source | Cross-check before spending |
| Bankable yield report | Lender-accepted dataset, ideally checked against nearby ground data | Lenders ask for traceable, validated data |
| Large plant with long horizon | Add an on-site station | Site data reduces uncertainty over time |
This table is our working practice, not a lender rule. Confirm the data requirement with the lender or independent engineer early.
How to use irradiance data in a design
- Pull at least two sources for the site coordinates.
- Compare annual and monthly GHI. Investigate any gap above a few percent.
- Check the diffuse share. A high DHI site gains less from steep tilt or trackers.
- Use the chosen dataset in the yield model, with its uncertainty stated.
- Carry that uncertainty into the P50 and P90 figures. Our P50 and P90 guide explains how.
Irradiance also sets tilt. See our tilt angle table for Indian cities for how the optimum moves with latitude and monsoon cloud.
FAQ
Which Indian region has the best solar irradiance?
Among the ten cities we checked, Jodhpur posts the highest GHI and DNI, followed by Ahmedabad. Check site-level data before choosing land.
Is NASA POWER data good enough for a DPR?
For screening, yes. For a bankable DPR, use a lender-accepted dataset and explain your choice. NASA POWER’s coarse grid misses local effects.
Can I get NIWE SRRA data?
NIWE runs the network under MNRE. Contact NIWE directly for current access terms and station coverage near your site.
Why is DHI so high in India compared with deserts elsewhere?
Aerosols, dust, and humidity scatter sunlight. Monsoon cloud adds more. The result is a large diffuse share even at sunny sites.
Get a site resource check
Heaven Designs runs irradiance cross-checks and PVsyst yield reports for Indian sites. For new land, we combine this with site survey and feasibility work.
To scope a resource check or yield study, request a project quote. We reply within 1 business day.