Merged Microwave Climate Data Record (CDR)

Air-Sea Essential Climate Variables (AS-ECV)

Motivation: Climate Trend Analysis

For this dataset, 14 satellite microwave radiometers flying over 33+ years have been carefully intercalibrated and merged to detect and analyze fundamental changes in Earth's climate system.

Description of the AS-ECV CDR

The AS-ECV CDR is a multi-decadal (currently 33+ years) Climate Data Record (CDR) for five Air-Sea (AS) variables, all of which are classified as Essential Climate Variables (ECV) by the Global Observing System for Climate. The AS-ECV CDR is on a global (but ocean only) 2.5° latitude by 2.5° longitude grid at monthly timesteps. Currently, the time period is from July 1987 through February 2021. The AS-ECV CDR will be extended every year as new observations come in. The data file format is CF-compliant NetCDF4. Table 1 gives the content of the AS-ECV CDR data file.

The AS-ECV CDR includes: sea-surface temperature SST (°C), near-surface ocean wind speed W (m/s), total water vapor above the ocean V (mm), total cloud liquid water L above the ocean (mm), and sea-surface rain rate R (mm/hr). Except for SST, all variables are derived from the microwave (MW) sensors shown in Figure 1. The SST is the Reynolds et al. [2007] product. MW SST retrievals do not start until 1998 with the launch of TMI and do not become global until 2002 with the launch of AMSR-E. To provide SST for the entire 1987 to 2020 period, we elected to use the Reynolds product. For the next release of the AS-ECV CDR, we plan to include the MW SST. The rain rate product is called Rain-MW to emphasize that it is derived solely from MW observations as compared to other rain rate products that rely on a variety of rain datasets.

During most of the AS-ECV CDR 33+ year time span, two or more sensors are in operation at any given time (Figure 1). During these overlap periods, the multi-sensor AS-ECV retrievals are averaged together into a single monthly map. In addition, RSS will continue to provide sensor-specific AS-ECV files, which contain observations from just one sensor. The sensor AS-ECV files are provided on a 0.25x0.25-degree grid at daily, 3-day, weekly, and monthly timesteps.

Table 1. Content of the AS-ECV Climate Data Record File



   ➢ Latitude

   Degrees North

   ➢ Longitude

   Degrees East

   ➢ Time

   Fractional Year

   ➢ Time_years

   Calendar Year

   ➢ Time_months

   Month of Year



   ➢ Sea-Surface Temperature (SST)


   ➢ Near-Surface Wind Speed


   ➢ Columnar Water Vapor


   ➢ Columnar Cloud Liquid Water


   ➢ Sea-Surface Rain Rate (Rain-MW)


Figure 1. Microwave radiometers (with their mission timelines) that are used in constructing the AS-ECV CDR. The dashed lines indicate future sensors that we plan to incorporate in the CDR. The blue lines indicate sensors in polar orbits and the red lines are sensors in inclined orbits.

Scientific Basis: Forward Model and Retrieval Algorithm

The AS-ECV algorithm uses brightness temperatures measurements (TB) from MW radiometers to estimate the AS-ECVs for grid cells across Earth’s surface. The algorithm is based on a precise Ocean Radiative Transfer Model (ORTM) for the ocean surface and intervening atmosphere. The following papers and reports describe the development of the ORTM: Wentz [1997], Wentz and Meissner [2000], Meissner and Wentz [2002], and Meissner and Wentz [2004]. The current status of the ORTM is given by Meissner and Wentz [2012], Meissner et al. [2014], and Wentz and Meissner [2016]. The same retrieval algorithm is used for all sensors. The retrieval algorithm can be thought of as the inverse function of the ORTM. It finds the set of AS-ECVs that closely matches the observed brightness temperatures:


The algorithm uses a two-stage regression approach to accomplish this, which is described by Wentz and Meissner [2000] and Wentz and Meissner [2007]. The two-stage regression algorithm finds SST, W, V, and L. Given these retrievals the rain algorithm finds rain rate R. The rain algorithm is described by Wentz and Spencer [1998] and Hilburn and Wentz [2007]. The combination of the two-stage regression and the rain algorithm is called a Unified Microwave Ocean Retrieval Algorithm (UMORA).

Description of Variables


Sea-Surface Temperature

Sea-surface temperature (SST) is a measure of the temperature in the upper column of ocean water (°C) and is a fundamental quantity that drives and interacts with the coupled ocean-atmosphere system. There are three main types of SST measurements. First, is the bulk SST which measures temperatures at depths on the order of meters. Second, is the sub-skin SST which measures temperatures at depths on the order of millimeters. Third, is the skin SST which measures temperatures at depths on the order of microns. Currently, we use a Reynolds value for SST so that the measurements span the 33+ year time period [Reynolds et al., 2007]. The Reynolds estimate is a bulk SST that merges observations from infrared and microwave satellites (AVHRR and AMSR) and in situ platforms (ships and buoys). In future versions of the data file we will include SSTs from microwave radiometers, which measure at sub-skin depths, using the ORTM-trained regression. These microwave-derived SSTs are sensitive to low frequency microwave measurements of TB (4-11 GHz) that extend from 1997 to the present. SST measurements are used to observe changes in the global climate system and help to predict decadal climate patterns like the El Niño Southern Oscillation. Moreover, SST constitutes 71% of the surface area input into merged global land-ocean surface temperature data products, which are used in forecasting ocean events such as hurricane trajectories [NCAR, 2014]. Recently SSTs have been used to train climate models so that their outputs match observations [Kosaka and Xie, 2013]. Beyond climate modeling and seasonal monitoring/forecasting, SST observations are useful for predicting coral bleaching, tracking pollution, and managing tourism and commercial fishery industries.

Near-Surface Wind Speed

Wind speed (m/s) measures the movement of air that is caused by the difference between high- and low-pressure systems due to differential heating and the rotation of the planet. In this dataset, we use the ORTM regressions to provide wind speeds at 10 m above the ocean surface with observed TBs that measure ocean surface roughness. Our newest wind algorithm now computes wind speed through rain and this winds-through-rain product will be included in future releases of the dataset. Wind speed is an important variable for monitoring weather and climate. Measuring wind speed is particularly important in the context of natural disasters where higher wind speeds contribute to more intense hurricanes and wildfires. On longer timescales and over larger areas, winds directly control the transport of air masses, with influences on seasonal (monsoons) and interannual (ENSO, Indian Ocean Dipole, North Atlantic Oscillations) climate variability. In addition, climate models benefit from wind speed measurements because winds not only contribute to climate oscillations, but also winds, specifically the Westerlies, drive the ocean currents that carry warm subtropical water to the polar regions. Finally, variations in wind speed can impact ecosystems. For example, winds carry nutrient-rich dust from the African Sahara to the Americas, which can have a positive influence on Amazonia primary productivity while having a negative influence on Florida’s coral reefs [Garrison et al., 2003; Shinn et al., 2000; Bristow et al., 2010; Yu et al., 2015].

Columnar Water Vapor

Water vapor is the gaseous, transparent state of water in the atmosphere. We measure the columnar water vapor above the ocean in mm, which represents the total height of atmospheric water vapor if it were condensed and spread out evenly across the 2.5x2.5 degree grid cell. In this dataset, we use the ORTM regressions with observed TBs to provide columnar water vapor above the ocean surface. The amount of water vapor in the atmosphere has implications for both weather phenomenon and climate studies. In the case of weather, water vapor is the medium for convection which transforms the sun’s heat energy to mechanical wind energy. In this process, water vapor evaporates from the ocean surface and lofts into the upper atmosphere, displacing the colder air. The subsequent condensation of water-laden air creates clouds and releases latent heat, both of which lead to tropical cyclones and thunderstorms. In addition, high water vapor content is integral to lightning development. Finally, water vapor is the most potent of the greenhouse gases. Increasing water vapor contributes to rising atmospheric temperatures which, in turn, will increase evaporation and the amount of water vapor that the atmosphere can hold.

Columnar Cloud Liquid Water

Cloud liquid water represents the liquid, opaque state of water in the atmosphere, i.e. clouds. It does not measure the solid forms of water, such as snow and ice in a cloud. Similar to water vapor, the cloud liquid water content is measured in mm as the height of liquid cloud water spread evenly across a 2.5x2.5-degree grid cell. In this case, the liquid water content is easy to distinguish from the ocean and water vapor given its small degree of polarity. We use the ORTM regressions with observed TBs to estimate columnar cloud liquid water above the ocean surface. Having an accurate measurement of cloud liquid water is essential to measuring Rain-MW precipitation. In addition, microwave-derived cloud masks are often used for infrared and visible satellite measurements, which cannot see through clouds. In terms of climate, clouds can have competing effects, some of which cool the earth by reflecting visible light while others warm the earth by absorbing infrared radiation.

Sea-Surface Rain Rate (Rain-MW)

The sea-surface rain rate measures the average ocean liquid water precipitation in mm/hr. The fundamental observable for the UMORA rain algorithm is the total columnar liquid water L (mm). We use L as input into an analytical solution for rain rate (R), which separates R from L when L is > 0.18 kg/m2. Atmospheric rivers and monsoonal rainfall are essential to supplying the world’s population centers with fresh water [Arabzadeh et al., 2020; Guan et al., 2010; Dettinger et al., 2011; NCAR, 2021; Zhiseng et al., 2014]. In addition, accurate measurements of rain allow us to better characterize droughts, landslides, floods, and severe storms, which have enormous impacts on society. It is critical to be able to understand the interannual variability and long-term trends and uncertainties of global rainfall over the ocean in the context of climate change. Observations indicate the narrowing and strengthening of precipitation in the ITCZ over recent decades in both the Atlantic and Pacific basins, with little change in its location [Byrne et al., 2018]. In addition, evidence suggests that increased atmospheric moisture will enhance the intensity of atmospheric river precipitation, with substantially longer and wider atmospheric rivers than the ones we observe today [Payne et al., 2020; Espinoza et al., 2018].


Arabzadeh A., M.R. Ehsani, B. Guan, S. Heflin, and A. Behrangi, 2020: Global Intercomparison of Atmospheric Rivers Precipitation in Remote Sensing and Reanalysis Products. Journal of Geophysical Research: Atmospheres, 125, e2020JD033021.

Bristow, C.S., K.A. Hudson-Edwards, & A. Chappell, 2010: Fertilizing the Amazon and equatorial Atlantic with West African dust. Geophysical Research Letters, 37(14).

Byrne M.P., A.G. Pendergrass, A.D. Rapp, & K.R. Wodzicki, 2018: Response of the Intertropical Convergence Zone to Climate Change: Location, Width, and Strength. Current Climate Change Reports, 4, pp. 355-370.

Dettinger, M.D., F.M. Ralph, T. Das, P.J. Neiman, & D.R. Cayan, 2011: Atmospheric rivers, floods and the water resources of California. Water, 3(2), pp. 445-478.

Espinoza, V., D.E. Waliser, B. Guan, D.A. Lavers, & F.M. Ralph, 2018: Global Analysis of Climate Change Projection Effects on Atmospheric Rivers. Geophysical Research Letters, 45(9), pp. 4299-4308.

Garrison, V.H., E.A. Shinn, W.T. Foreman, D.W. Griffin, C.W. Holmes, C.A. Kellogg, M.S. Majewski, L.L. Richardson, K.B. Ritchie, & G.W. Smith, 2003: African and Asian Dust: From Desert Soils to Coral Reefs. BioScience, 53(5), pp. 469-480.[0469:AAADFD]2.0.CO;2.

Guan, B., N.P. Molotch, D.E. Waliser, E.J. Fetzer, & P.J. Neiman, 2010: Extreme snowfall events linked to atmospheric rivers and surface air temperature via satellite measurements. Geophysical Research Letters, 37, L20401.

Hilburn, K.A., & F.J. Wentz, 2007: Intercalibrated passive microwave rain products from the unified microwave ocean retrieval algorithm (UMORA). Journal of Applied Meteorology and Climatology, 47, pp. 778-794.

Kosaka, Y. & S. Xie, 2013: “Recent Global-Warming Hiatus Tied to Equatorial Pacific Surface Cooling.” Nature Letter. 501.

Meissner, T., and F.J. Wentz, 2002: An updated analysis of the ocean surface wind direction signal in passive microwave brightness temperatures. IEEE Transactions on Geoscience and Remote Sensing, 40(6), pp. 1230-1240.

Meissner, T., and F.J. Wentz, 2004: The complex dielectric constant of pure and sea water from microwave satellite observations. IEEE Transactions on Geoscience and Remote Sensing, vol. 42(9), pp. 1836-1849.

Meissner, T., and F.J. Wentz, 2012: The emissivity of the ocean surface between 6 - 90 GHz over a large range of wind speeds and Earth incidence angles. IEEE Transactions on Geoscience and Remote Sensing, 50(8), pp. 3004-3026.

Meissner, T., F.J. Wentz, and L. Ricciardulli, 2014: The emission and scattering of L-band microwave radiation from rough ocean surfaces and wind speed measurements from the Aquarius sensor. Journal of Geophysical Research: Oceans, 119, pp. 6499-6522.

National Center for Atmospheric Research (NCAR), 2014: The Climate Data Guide: SST Data Sets: Overview & Comparison Table.

National Center for Atmospheric Research (NCAR), 2021: Monsoons. University Corporation for Atmospheric Research (UCAR) Center for Science Education.

Payne, A.E., M. Demory, L.R. Leung, A.M. Ramos, C.A. Shields, J.J. Rutz, N. Siler, G. Villarini, A. Hall, & F.M. Ralph, 2020: Responses and impacts of atmospheric rivers to climate change. Nature Reviews Earth & Environment, 1, pp. 143-157.

Reynolds, R.W., T.M. Smith, C. Liu, D.B. Chelton, K.S. Casey, & M.G. Schlax, 2007: Daily high-resolution blended analyses for sea surface temperature. Journal of Climate, 20, pp. 5473-5496.

Shinn, E.A., G.W. Smith, J.M. Prospero, P. Betzer, M.L. Hayes, V. Garrison, & R.T. Barber, 2000: African Dust and the Demise of Caribbean Coral Reefs. Geophysical Research Letters, 27(19), pp. 3029-3032.

Wentz, F.J., 1997: A well-calibrated ocean algorithm for special sensor microwave/imager. Journal of Geophysical Research, 102(c4), pp. 8703-8718.

Wentz, F.J. & T. Meissner, 2000: AMSR Ocean Algorithm, Version 2; Report No. 121599A-1, 66 pp., Remote Sensing Systems, Santa Rosa, CA. Available online:

Wentz, F.J. & T. Meissner, 2007: AMSR-E Ocean Algorithms; Supplement 1. Report No. 051707, 6 pp., Remote Sensing Systems, Santa Rosa, CA. Available online:

How to Cite These Data

Continued production of this data set requires that we demonstrate the value of this data set to the scientific community.  Please cite these data when used in your publications:

Wentz, F.J., and the RSS team, 2021: Air-Sea Essential Climate Variables Climate Data Record, Monthly data on 2.5 deg grid, Version 8.1, Remote Sensing Systems, Santa Rosa, CA. Data are available at