What is CCMP?
The Cross-Calibrated Multi-Platform (CCMP) is a gridded Level 4 (L4) product that provides vector wind over the world's oceans. CCMP is a combination of ocean surface (10m) wind retrievals from multiple types of satellite microwave sensors and a background field from reanalysis. The resulting product is a spatially complete dataset available every six hours that remains closely tied to the satellite retrievals where they are available and closely collocated in time and space. Where satellite retrievals are not available, CCMP is statistically consistent with satellite winds. Creating a L4 product using this method of combining satellite and reanalysis data ensures a smooth transition in the wind field between regions with and without satellite retrievals.
What satellites are included?
CCMP includes most of the wind-sensing U.S., Japanese, and European satellites flown to date. This includes the scatterometers QuikScat and ASCAT-A as well as the SSM/I, SSMIS, TMI, GMI, ASMR-E, AMSR2, and WindSat radiometers. Winds from ASCAT-B and ASCAT-C were withheld from the current version of CCMP (V3.0) so that they can serve as an independent source of winds to validate the product.
What is the background field?
CCMP V3.0 used ERA5 10m Neutral Stability (NS) winds as the background field. Note that several substantial adjustments (described briefly below) were applied to the ERA5 winds before they were used in the analysis.
Introduction, Motivation, and History
Gap-free ocean surface wind data of high quality and high temporal and spatial resolution are useful for a variety of purposes and are necessary for studying large scale air-sea interactions that affect the atmosphere and the ocean. Ocean vector winds are dynamic and continuously evolve over short time and length scales. This characteristic makes the production of global, gridded, gap-free wind fields a challenge, especially at temporal scales of less than one day. Accurate research requires consistent ocean vector wind data for a long enough time period to resolve wind-induced patterns such as the El Niño-Southern Oscillation (ENSO) and the Madden-Julian Oscillation (MJO).
Remote Sensing Systems has invested many years of research into validating and cross-calibrating passive and active microwave wind retrievals from satellites. The "raw" satellite data contain gaps (of the order of a few hundred km) between satellite swaths and are available at irregular times. Furthermore, satellites are continuously being launched and decommissioned. This means that the total amount of available satellite data can vary from year to year. This all complicates any research efforts using satellite winds.
The motivation for CCMP is to merge all satellite data together to produce spatially complete wind maps at evenly spaced times. To do this, we use a Variational Analysis Method (VAM) of data assimilation developed by Hoffman, Atlas, and co-workers [Atlas et al. 1996, 2011; Hoffman et al, 2003] which has been shown to be a dynamically suitable way to combine satellite observations into gap-free wind fields. The VAM generates a gridded surface wind analysis which minimizes an objective function measuring the misfit of the analysis to the background. The data are subject to a number of smoothness and dynamical constraints.
The CCMP Version 3.0 (V3.0) data product described here is a continuation of the widely-used original CCMP product and builds on decades of careful VAM and dataset development. CCMP V3.0 is more completely described in [Mears et al., 2022]. Information from this peer-reviewed manuscript is summarized here.
The development of V3.0 was driven by several goals:
- Use a more up-to-date and regularly updated reanalysis product for the background wind field. Production of the ERA-Interim reanalysis ceased in July, 2019, forcing us to adopt a new background if we wanted to be able to extend CCMP beyond this time. We chose to move to ERA5 because it is available hourly, facilitating a future enhancement to CCMP for higher frequency analysis.
- Improve performance and agreement with satellite winds at high wind speed.
- Minimize spurious trends caused by the interaction between the amount of satellite measurements available and the satellite/model biases.
The development philosophy we adopted assumes that the RSS-produced wind datasets from scatterometers (QuikSCAT and ASCAT) are accurate at all wind speeds. These winds have been validated via comparison with winds from moored buoys at low and moderate wind speeds. Above ~20 m/s buoy winds become increasingly less reliable. RSS scatterometer winds are validated at high winds in by:
- comparison with airborne Stepped Frequency Microwave Radiometer (SFMR) measurements flown in tropical cyclones, which are, in turn, anchored by wind speeds from dropsondes [Meissner et al 2017]. The SMAP winds validated in [Meissner et al 2017] are compared to ASCAT winds in [Ricciardulli et al. 2021].
- comparison with winds measured by Saildrones in tropical cyclones [Ricciardulli et al. 2022].
- comparison with winds from oil platforms in the North Sea [Manaster et al 2019].
Our work revealed that sources of wind data (radiometer derived wind speeds and the ERA5 background winds) had systematic biases relative to scatterometer wind. In particular, ERA5 winds were systematically biased low relative the RSS scatterometer winds. These systematic biases were removed before including ERA5 winds and radiometer winds in the analysis.
What's new in V3.0?
- V3.0 uses ERA5 Neutral Stability (NS) winds as a background field. V2.0 used unadjusted ERA-Interim 10m winds as its background field.
- ERA5 NS winds are adjusted to account for ocean surface currents using the Ocean Surface Current Analysis Real-time (OSCAR) dataset. Satellite winds are measured relative to the moving ocean surface.
- A speed adjustment was applied to match the distribution of wind speeds to QuikSCAT and ASCAT measurements. The adjustment depends on time of year and latitude, but does not change from year to year, so it has no direct effect on long-term trends.
- A small seasonally dependent regional vector adjustment was applied to improve agreement between the adjusted ERA5 NS winds and scatterometers.
- Small regional adjustments were applied to radiometer winds before they are included. These adjustments are the largest for the "medium frequency" radiometers whose lowest frequency channel is 19 GHz (e.g., SSM/I and SSMIS). The 19 GHz winds are more strongly affected by anomalous atmospheric conditions than wind retrievals that use the lower 11 GHz channel. These adjustments were not used in CCMP V2.0.
- Because of the dependence on OSCAR ocean currents, CCMP 3.0 starts in 1993 when the OSCAR dataset begins.
These changes improve the accuracy of CCMP, particularly at winds above 15 m/s. The long-term trends are also more reliable and agree better with other long-term records of wind speed. Earlier versions of CCMP likely included spurious wind trends due to the mismatch between the satellite and background winds.
CCMP Dataset Structure
4x Daily (6 hourly)
- The L4.0 CCMP V3.0 products consist of daily files containing four daily maps (00, 06, 12, and 18Z) of each variable. The files are in netCDF-4 format with CF-1.8 compliant metadata. Note that winds are provided for both ocean and land regions. The winds over land are from ERA5 but were subjected to the same adjustments as the oceanic winds which are unlikely to be correct for land surfaces. Therefore, we do not recommend using winds over land.
The variables in the L4.0 files are:
|ws||10m wind speed, 10m above the ocean surface||m/s|
|uwnd||10m zonal wind (U)||m/s|
|vwind||10m meridional wind (V)||m/s|
|nobs||number of satellite retrievals included||-|
The U and V components are relative to true north and use the oceanographic direction convention.
- The L4.5 CCMP V3.0 products contain monthly winds averaged over the calendar month as well as anomalies relative to a 1995-2014 baseline. Vector averages are computed for U and V. The average wind speed W is computed using a scalar average.
The variables in the L4.5 files are:
|u||mean 10m zonal wind (U)||m/s|
|u_anom||10m zonal wind anomaly||m/s|
|v||mean 10m meridional wind (V)||m/s|
|v_anom||10m meridional wind anomaly||m/s|
|w||mean 10m wind speed||m/s|
|w_anom||10m wind speed anomaly||m/s|
|n||number of satellite retrievals included||-|
|Coriolis||WindSat||V7.0.1 All Weather|
|Mears, C.; Lee, T.; Ricciardulli, L.; Wang, X.; Wentz, F., 2022: RSS Cross-Calibrated Multi-Platform (CCMP) 6-hourly ocean vector wind analysis on 0.25 deg grid, Version 3.0, Remote Sensing Systems, Santa Rosa, CA. Available at www.remss.com https://doi.org/10.56236/RSS-uv6h30|
|Mears, C.; Lee, T.; Ricciardulli, L.; Wang, X.; Wentz, F., 2022: RSS Cross-Calibrated Multi-Platform (CCMP) monthly ocean vector wind analysis on 0.25 deg grid, Version 3.0, Remote Sensing Systems, Santa Rosa, CA. Available at www.remss.com https://doi.org/10.56236/RSS-uv1m30|
The Remote Sensing Systems Cross-Calibrated Multi-Platform (CCMP) ocean vector wind analysis products on 0.25 deg grid, Version 3.0 by Remote Sensing Systems is licensed under a Creative Commons Attribution 4.0 International License.
- Mears, C.; Lee, T.; Ricciardulli, L.; Wang, X.; Wentz, F. Improving the Accuracy of the Cross-Calibrated Multi-Platform (CCMP) Ocean Vector Winds. Remote Sens. 2022, 14, 4230. https://doi.org/10.3390/rs14174230
- Atlas, R., R. N. Hoffman, J. Ardizzone, S. M. Leidner, J. C. Jusem, D. K. Smith, D. Gombos, 2011: A cross-calibrated, multiplatform ocean surface wind velocity product for meteorological and oceanographic applications. Bull. Amer. Meteor. Soc., 92, 157-174. https://doi.org/10.1175/2010BAMS2946.1