1:45 PM - 3:15 PM
[ACG30-P21] Ensemble data assimilation of ocean surface wind estimated by seabird biologging
Keywords:Biologging, Data assimilation
Monitoring ocean and atmospheric conditions on the sea surface is indispensable for understanding atmosphere-ocean interaction, but direct observations are limited to those using small ships or buoys. Although ocean surface wind observed by weather satellites has been widely used, its spatio-temporal resolution is not enough and there is a significant gap in the ocean observing system. These days, biologging, which has developed in ethology, has been attracting as a new way of meteorological observation to fill the gap. Biologging is a method to measure animals’ movement or their environmental conditions by attaching a small data logger to their body. Recently the loggers have been getting smaller and lighter, and becoming able to measure a variety of parameters.
In general, birds fly in response to winds, so their flight paths include information on the wind. Considering this feature, a wind estimation method was proposed. This is an approach to solve inverse problems, simultaneously estimating bird’s orientation and their environmental wind from bird’s flight paths. In this research, we used ocean surface winds estimated by this method using GPS positional data of streaked shearwaters which build a nest in Awashima island, Niigata prefecture, Japan, in the summer season. We conducted an Observing System Experiment (OSE) , in which we ran analysis-forecast cycles with and without the bird-wind data and examined those observation impacts. We call these two experiments, BIRD and CTRL, respectively.
We used the regional reanalysis system NHM-LETKF, which combined JMA’s non-hydrostatic model (NHM) with the local ensemble transform Kalman filter (LETKF) . Horizontal resolution of the model was 25km and 5km, and one-way double nesting was applied. The ensemble size was set to 30. Calculation period is from Aug. 1st to Sep. 5th in 2018, and bird wind data exists from Aug. 22nd to Sep. 5th. Analysis was done every 6 hours. The number of wind data estimated by 4 birds is 727. In addition, conventional observation data (NCEP PREPBUFR) were assimilated. Bird wind was treated as 2 m above sea level, and we regarded the observational error as the same as the estimation error output from the wind estimation model.
First, we examined bird wind accuracy by comparing it with JMA’s MSM analysis (horizontal resolution is 5km) . Correlation coefficient was nearly 0.5, which indicated that estimation accuracy was good. Next, we compared the surface wind spread of BIRD with that of CTRL averaged from Aug. 22nd to Sep. 5th. Bird observation points were distributed along the coast from Japan-Sea to southern Hokkaido, and these observation impacts propagated to the northern part of Japan-Sea and the western pacific. Also, three typhoons passed on and around Japan during this period, and these central pressure or intensity differed from BIRD to CTRL. We will discuss the observation impacts on these typhoons.
Acknowlegements
This work was supported by Grant-in-Aid for Scientific Research (A) (Grant 22H00569) . We used the Earth Simulator (JAMSTEC) for the OSE experiment.
References
[1] Goto, Y., Yoda, K., & Sato, K. (2017). Asymmetry hidden in birds’ tracks reveals wind, heading, and orientation ability over the ocean. Science advances, 3(9), e1700097.
[2] Fukui, S., Iwasaki, T., Saito, K., Seko, H., & Kunii, M. (2018). A feasibility study on the high-resolution regional reanalysis over Japan assimilating only conventional observations as an alternative to the dynamical downscaling. Journal of the Meteorological Society of Japan. Ser. II., 96(6), 565-585.
Difference of surface U-wind, V-wind spread between BIRD and CTRL averaged from Aug. 22nd to Sep. 5th in 2018. Minus means the declining analysis spread by assimilating wind from birds. Green dots express all bird observation points in this period.
In general, birds fly in response to winds, so their flight paths include information on the wind. Considering this feature, a wind estimation method was proposed. This is an approach to solve inverse problems, simultaneously estimating bird’s orientation and their environmental wind from bird’s flight paths. In this research, we used ocean surface winds estimated by this method using GPS positional data of streaked shearwaters which build a nest in Awashima island, Niigata prefecture, Japan, in the summer season. We conducted an Observing System Experiment (OSE) , in which we ran analysis-forecast cycles with and without the bird-wind data and examined those observation impacts. We call these two experiments, BIRD and CTRL, respectively.
We used the regional reanalysis system NHM-LETKF, which combined JMA’s non-hydrostatic model (NHM) with the local ensemble transform Kalman filter (LETKF) . Horizontal resolution of the model was 25km and 5km, and one-way double nesting was applied. The ensemble size was set to 30. Calculation period is from Aug. 1st to Sep. 5th in 2018, and bird wind data exists from Aug. 22nd to Sep. 5th. Analysis was done every 6 hours. The number of wind data estimated by 4 birds is 727. In addition, conventional observation data (NCEP PREPBUFR) were assimilated. Bird wind was treated as 2 m above sea level, and we regarded the observational error as the same as the estimation error output from the wind estimation model.
First, we examined bird wind accuracy by comparing it with JMA’s MSM analysis (horizontal resolution is 5km) . Correlation coefficient was nearly 0.5, which indicated that estimation accuracy was good. Next, we compared the surface wind spread of BIRD with that of CTRL averaged from Aug. 22nd to Sep. 5th. Bird observation points were distributed along the coast from Japan-Sea to southern Hokkaido, and these observation impacts propagated to the northern part of Japan-Sea and the western pacific. Also, three typhoons passed on and around Japan during this period, and these central pressure or intensity differed from BIRD to CTRL. We will discuss the observation impacts on these typhoons.
Acknowlegements
This work was supported by Grant-in-Aid for Scientific Research (A) (Grant 22H00569) . We used the Earth Simulator (JAMSTEC) for the OSE experiment.
References
[1] Goto, Y., Yoda, K., & Sato, K. (2017). Asymmetry hidden in birds’ tracks reveals wind, heading, and orientation ability over the ocean. Science advances, 3(9), e1700097.
[2] Fukui, S., Iwasaki, T., Saito, K., Seko, H., & Kunii, M. (2018). A feasibility study on the high-resolution regional reanalysis over Japan assimilating only conventional observations as an alternative to the dynamical downscaling. Journal of the Meteorological Society of Japan. Ser. II., 96(6), 565-585.
Difference of surface U-wind, V-wind spread between BIRD and CTRL averaged from Aug. 22nd to Sep. 5th in 2018. Minus means the declining analysis spread by assimilating wind from birds. Green dots express all bird observation points in this period.