Trmm data download free

View the full list of frequently asked questions. Data News. Friday, January 7, Users of those products should kept this impact in mind when using data beyond hour 15 on 5 Jan Read More. Friday, December 3, When radar data resumes it will be V07A which has major changes in swath organization as well as science content.

Wednesday, December 1, The GPM near realtime will be transitioning its software to product V07 radar products. The V07 radar products have a different format from V The radiometer products will remain at their current V05 until at least February Wednesday, June 23, Please see the schedule below for the specific details and times.

Related Articles. Wednesday, September 9, How much rain and snow fall on Earth in any given year? NASA scientists are answering this question more accurately than ever before and observing precipitation in the most remote places on Earth. Thursday, October 17, Accurate and reliable precipitation records are not only crucial to understanding trends and variability but also for water management resources and food security, ecological management, and weather, climate and hydrological forecasting.

The point of handing out the data at fine scales with large uncertainty is that the user has the freedom to create averages in ways that best correspond to the problem at hand. At the fine scales in full-resolution 3B42, we usually observe that random error dominates the error budget — that is, the part of the error that averages out with sufficiently long averaging. Wang, J. Adler, G. Huffman, D.

It is fair to say that error estimates are lagging behind where we expected them to be. However, recent work shows some promise of getting a handle on error estimation, for example using the work by Maggioni and collaborators:. Maggioni, V. Sapiano, R. Adler, Y. Tian, G. There is some hope that this approach will naturally encompass both random and bias errors.

Such detail will not be suitable for all users, so we will need to produce a simplified error estimate as well. Behrangi, A. Stephens, R. Huffman, B. Lambrigtsen, M. Climate , 27 11 , doi: See the accompanying Technical Notes and links therein for a description of the steps involved in producing the TMPA products.

After the preprocessing is complete, the 3-hourly multi-satellite fields are summed for the month and combined with the monthly accumulated Global Precipitation Climatology Centre GPCC rain gauge analysis using inverse-error-variance weighting to form a monthly best-estimate precipitation rate, which is TRMM Product 3B The final step is to scale all the 3-hourly estimates for the month to sum to the monthly value for each gridbox separately. Limits are imposed on the scaling to avoid unphysical results, so particularly in low-rain areas, the 3B42 values in a month may not sum exactly to the corresponding 3B For this reason, 3B43 is the preferred dataset for monthly values.

The TRMM data system was designed to provide episodic, complete reprocessings of the TRMM products to take advantage of ongoing research to improve the various scientific algorithms. Huffman, G. This release incorporated several important changes as part of the upgrade to Version The complete Version 6 archive was maintained for public access throughout and beyond the cutover to Version 7. For Initial Processing IP , our switch to the GPCC gauge analysis necessitates increasing the latency of the products from 10 days after the month, which was the case in Version 6, to about two months after the end of the month.

The initial Version 7 release occurred in May , while the second set was posted in December Initial testing showed that the revisions eliminated the unrepresentatively low bias in ocean values for that were related to how we treated an early version of AMSU-based precipitation in Version 6.

The basis for this difference is not explicated at this point, but the value is small enough and consistent enough that we chose to release the data.

Note: As discussed below, up through September calibration to TCI was computed on a month-to-month basis. Thereafter, the shutdown of the PR required a climatologically based calibration. TRMM ran out of fuel in mid and began a slow descent.

As well, there were some battery issues. By 7 October , the satellite descended to an altitude that precluded useful radar data, which necessitated a different calibration procedure for 3B42 starting with October data.

This implies at least a slight inhomogeneity, primarily over the oceans because we know that calibrations involving PR have a different interannual behavior than calibrations based solely on passive microwave.

We recognized that the real-time version of 3B42, 3B42RT, had a strong application focus, and expect to run it until the equivalent IMERG products are satisfactory, which happened in mid Undoubtedly, users wanted the old product forever, but at some point we had to stop. This is equally true for all runs, including the Final. These pages include documentation about the data sets and algorithms, and contact information. TRMM 3B42 is a high-resolution in both time and space gridded record of precipitation.

The rain rates are not instantaneous, but rather add up to monthly rainfall estimates found in TRMM 3B The product is at version 7 at the time of writing. The measurements that go into the TRMM 3B42 precipitation estimates are from the TRMM passive microwave radiometer and precipitation radar, infrared radiometers, infrared brightness temperatures from geostationary satellites, and rain gauge measurements in a final step.

The TRMM data are used to make monthly IR calibration tables, which are applied to the IR geostationary data to obtain the 3-hourly data, and then rescaled to agree with co-located microwave measurements and monthly rain gauge observations. Typical research applications of this dataset include examining the climatological distribution of rainfall, and its frequency and intensity e. It can be used for validation of the distribution of tropical rain in climate models e. Because of its high temporal resolution, the dataset can be used to explore the diurnal cycle e.

The key limitation of this dataset like all merged-satellite precipitation products is the indirect and complex nature of translating sparse satellite precipitation measurements into high-resolution gridded precipitation estimates. Satellites can only indirectly measure quantities related to rain rate at the surface: microwave and infrared satellites measure brightness temperature, which is then converted to rain rate indirectly, while radars measure energy reflected by cloud and rain drops throughout the depth of the column.

Then, these indirect measurements along with direct gauge measurements over land are used as an input to a complex algorithm that produces estimates of surface rain rate on a regular grid in time and space.

See Huffman for a complete description. However, these methods cannot account for the structural uncertainty in measuring precipitation, especially over the ocean where there is very limited opportunity for ground validation. Precipitation rate depends strongly on resolution in time and space, which is the source of the most common errors processing and interpreting this and other precipitation datasets see Chen and Knutson for a thorough analysis of the effect of resolution on precipitation extremes.

Errors in calculation and interpretation abound when precipitation datasets of different spatial resolutions are compared. Rain rates, and especially the distribution of rain rates at a particular location, depend strongly on the spatial resolution of the dataset.

For example, a measure of extreme rain e. In order to compare two precipitation datasets on grids with different resolutions, one should typically regrid one or both of the datasets to a common grid using a regridding method that conserves the total amount of rain falling in an area. Often, the default interpolation in an analysis software package is bilinear interpolation e. One conservative regridding method is described in Jones The uniform spatial grid of this dataset lends itself to comparison with climate models, as long as care is taken to put the data on comparable grids.

The model precipitation data should be accumulated, rather than instantaneous, precipitation rate for comparison with TRMM 3B You signed in with another tab or window. Reload to refresh your session.

You signed out in another tab or window. Other types could be "gaugeRelativeWeighting. The VRT-file contains all the downloaded binary files with the appropriate. Within R, the script trmm. This is flipped in y direction and the files written. This Geotiff contains all the layers for and can.