Characterizing AMV Height-Assignment Error by Comparing Best-Fit Pressure Statistics from the Met Office and ECMWF Data Assimilation Systems

Abstract

To ensure realistic use of atmospheric motion vector (AMV) observations in data assimilation, the error characteristics of the observation type need to be known and carefully taken into account. Assigning a height to the tracked feature is one of the most significant error sources for AMV observations. In this article, the characteristics of the AMV height-assignment error are studied by comparing model best-fit pressure statistics between the Met Office and ECMWF data assimilation systems. The aim is to provide detailed uncertainty estimates for the assigned pressure and to demonstrate that the best-fit pressure enables reliable estimation of the uncertainties in the AMV height assignment. Typical values for the standard deviation of the difference between the assigned pressure and the best-fit pressure are 50–80 hPa at high levels, 115–165 hPa at midlevels, and 60–125 hPa at low levels, depending on satellite, channel, and height-assignment method. Observed minus best-fit pressure biases are mostly within the range of ±50 hPa. The results are very similar for the Met Office and ECMWF systems, suggesting that the pressure differences are not strongly dependent on the data assimilation system. Furthermore, the findings are in good agreement with the expected characteristics of the height-assignment methods and quality of the observations. Thus, best-fit pressure statistics give reliable information about the uncertainties in the AMV height assignment.

Assessment of MISR Cloud Motion Vectors (CMVs) Relative to GOES and MODIS Atmospheric Motion Vectors (AMVs)

“…Relative to the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), infrared (IR) and visible (VIS) channel AMV heights exhibit mean differences as large as 2.0 km and standard deviations as large as 3.4 km, depending on the opacity and homogeneity of tracked clouds (Di Michele et al 2013). Comparison of AMV-assigned heights with height of best fit with model demonstrates AMV-model height differences that are consistent with the lidar results (Salonen et al 2015), and comparison of AMV heights with the height of best agreement with rawinsonde profiles suggests that height assignment errors represent 70% of AMV uncertainty ). Characterization and reduction of height assignment error continues to be aggressively investigated by the NWP community.…”

Evolution of the upper tropospheric outflow in Hurricanes Iselle and Julio (2014) in the Navy Global Environmental Model (NAVGEM) analyses and in satellite and dropsonde observations

“…Additional information on AMVs and their potential errors can be found in Salonen et al . [], Velden et al . [], Menzel [], Nieman et al .…”

“…The AMVs have global coverage at the spatial and temporal resolutions of the parent satellite and are particularly useful over the open ocean where other observations are scarce. Additional information on AMVs and their potential errors can be found in Salonen et al [2015], Velden et al [2005], Menzel [2001], Nieman et al [1997], and Schmetz et al [1993]. Information on the assimilation of AMV observations into global and regional modeling systems can be found in Bormann et al [2012], Cotton and Forsythe [2012], Cress [2012], Pauley et al [2012], and Su et al [2012].…”

WIVERN: A New Satellite Concept to Provide Global In-Cloud Winds, Precipitation, and Cloud Properties

“…9. The AMV error is a combination of error in the tracking step and an error in speed due to an uncertainty in the height assignment as discussed by Salonen et al (2015); Forsythe and Saunders (2008) describe how the assigned AMV velocity error increases with the vertical wind shear in the model. For heights above 500 hPa and for all latitudes, the mean observation error is mostly in the range 5-9 m s −1 .…”