Kozo Okamoto 19th Coherent Laser Radar Conference

CLRC 2018, June 18 – 21 1

Evaluation of potential impacts of future Japan’s space-based

Doppler Wind Lidar (DWL) on polar- and tropical-orbiting

satellites

Kozo Okamoto(a,b), Toshiyuki Ishibashi(a), Shoken Ishii(b), Philippe Baron(b), Kyoka

Gamo(c), Taichu Tanaka(a), Koji Yamashita(d), Takuji Kubota(e)

(a) Meteorological Research Institute of Japan Meteorological Agency, Tsukuba, Japan

(b) National Institute of Information and Communications Technology, Kobanei, Japan

(c) FUJITSU FIP Corporation, Tokyo, Japan

(d) Meteorological Satellite Center of JMA, Tokyo, Japan

(e) Japan Aerospace Exploration Agency, Tsukuba, Japan

kokamoto@mri-jma.go.jp

Abstract: The feasibility of coherent Doppler wind lidars (DWLs) has been

investigated based on a sensitivity observation system simulation experiment (OSSE).

The pseudo-truth atmospheric field is generated from the Sensitivity Observing System

Experiment (SOSE) approach. Hourly aerosols are produced to simulate DWL by a

global aerosol chemical transport model in which wind field is nudged with pseudo-

truth. Simulated measurement of horizontal line-of-sight wind speeds is assimilated by

using the four-dimensional variational (4D-Var) scheme of the operational global data

assimilation system at Japan Meteorological Agency. We evaluated potential impacts

of DWLs onboard polar and low-inclination orbiting satellites in January and August in

2010. We found positive impacts of DWLs on either satellites and greater impacts in

the January experiment. The results also showed seasonal dependency of impacts and

importance of quality control and observation error setting.

Keywords: Data assimilation, OSSE, satellite

1. Introduction

The 3D global wind observations are essential for numerical weather prediction (NWP) but current

global observation network does not satisfy the NWP needs. A space-based Doppler wind lidar (DWL)

is one of good candidates and its feasibility study and development have been underway in many studies.

The Japanese scientific community is also investigating the impact of DWLs on NWP by using an

observing system simulation experiment (OSSE). Compared with many previous studies on the OSSE

for DWLs, this study simulated hour-by-hour aerosol fields and then realistic DWL data distributions

and quality information, which allowed us to discuss the importance of quality control and observation

error setting in a data assimilation system. The paper gives a brief description on our OSSE approach

and results of DWL assimilation. The detail is found in [1].

2. OSSE configuration

We adopted the OSSE based on a sensitivity observing system experiment (SOSE) approach [2] because

it could use all existing observations as opposed to a traditional nature-run OSSE that requires simulating

them. Our SOSE-OSSE configuration is shown in Fig.1. First, we simulated DWL winds and their

quality information such as the signal-to-noise ratio from a pseudo-truth (PT) atmospheric field, and

clouds and aerosols. The PT field was constructed by correcting an original initial (analysis) field using

the adjoint sensitivity structure and by merging it with existing observations. Clouds were generated

from the global forecast model through data assimilation cycle of the PT generation process. Aerosols

were calculated using a global chemical transport model [3] nudged with PT winds. PT atmospheric

field, clouds and aerosols were generated hourly and consistent each other.

Mo4

Kozo Okamoto 19th Coherent Laser Radar Conference

CLRC 2018, June 18 – 21 2

The end-to-end lidar simulator, named the Integrated Satellite Observation Simulator for a Space-Borne

Coherent Doppler Lidar (ISOSIM-L) calculates backscattered power, background noise power, signal-

to-noise ratio (SNR), noisy Doppler-shifted signal, retrieved winds in the direction of line-of-sight, and

retrieval quality information. With respect to DWL, we assumed two coherent receivers pointing toward

the ground at 35° off-nadir with azimuthal angles of 45° and 135° along the satellite track. Laser

wavelength, pulse energy and repetition frequency are 2.0 μm, 125 mJ, and 30 Hz, respectively. Target

vertical resolution and wind speed accuracy are set to 0.5 km and 1 m s−1 at an altitude between 0 and 3

km, 1 km and 3 m s−1 between 3 and 8 km, and 2 km and 3 m s−1 between 8 and 20 km. A candidate

platform for the DWL is a super-low-altitude satellite flying at 220 km or lower. The detail of ISOSIM-

L and DWL instruments are described in [4,5], respectively.

3. Data assimilation experiments

We assimilated simulated observations of horizontal line-of-sight (HLOS) wind speed. In the

preprocessing procedures of data assimilation, we removed the anomalous data flagged by ISOSIM-L,

low SNR data and data substantially departed from first-guess (short-range forecast). The quality control

(QC) procedures with rigid thresholds successfully removed DWL winds that disagreed from PT: 85 %

of all data were excluded (mainly data above 10 km and near the surface), of which 60 % by SNR-based

QC. Observation error R was defined with 220.1 mECR : E is measurement error estimated from

ISOSIM-L according to altitude and season. Other error components such as observation operator and

representativeness error were assumed to be 1.0 m s-1. Finally, we inflated the observation error by

multiplying an experimental factor of C (now set to 4.0) to avoid degradation of forecast skills through

data assimilation experiment. This degradation was probably attributed to inappropriate treatment of

oversampling of DWLs and PT error.

We conducted analysis–forecast cycle experiments to assess the impacts of the DWL data simulated by

ISOSIM-L in two one-month assimilation experiments in January and August 2010. The data

assimilation system was the low-resolution version of the operational global data assimilation system of

JMA as of 2010 [6]. The analysis system was an incremental 4D-Var method with an inner loop of

horizontal resolution of 120 km and outer loop of 60 km. We performed 6 h data assimilation cycles

from December 20, 2009, to February 7, 2010, (January experiment) and from July 20, 2010, to

September 9, 2010 (August experiment). We also ran 120 h forecasts at 12:00 UTC from January 1 to

31, 2010, for the January experiment and from August 1 to 31, 2010, for the August experiment. The

results are shown from three experiments with different observation configurations. One is a reference

experiment, denoted as CNTL, where all of the observations used in the operational system were

assimilated. The second experiment, (TESTP) assimilated DWL HLOS winds from the polar-orbiting

Figure 1. SOSE-OSSE scheme. The upper block is an offline simulation process that produces PT

atmospheric fields, clouds, and aerosols and then simulates DWL winds by using ISOSIM-L. The

lower block is a regular assimilation process, except for adding DWL simulations.

SOSE pseudo-truthand

forecasted cloud

Lidar simulator(ISOSIM-L)

Simulated WLOS, quality info

aerosol model (MASINGAR)

DWL wind simulation

wind aerosol

Existing observation

Simulated WLOS, quality info

assimilationfirst guess analysis forecast model

assimilationfirst guess

data assimilation cycle

Kozo Okamoto 19th Coherent Laser Radar Conference

CLRC 2018, June 18 – 21 3

satellite in addition to CNTL observations. The third experiment (TESTT) assimilated DWL HLOS

winds from tropical satellite with low-inclination of 35.1° in addition to CNTL observations.

Figure 2 shows the relative forecast error reduction of zonal wind for TESTP and TESTT in the January

experiment. The relative forecast error is defined with root mean square (RMS) difference from PT field

and normalized by CNTL RMS forecast error. Positive impact (error reduction) is evident in both

hemispheres and in the tropics through wide tropospheric layers over a broad forecast range up to day 5

for TESTP and TESTT experiments. Improvement is clear, particularly in the upper and lower

troposphere in the tropics, with statistical significance. Figure 3 shows that impacts for the August

experiment are also generally positive with some exceptions of negative impact in short-range forecasts

in the southern hemisphere for TESTP. Because we found simulated DWL quality was a little lower in

August (not shown), those data probably contaminated analyses and then forecasts even after the rigid

QC. This suggests that more appropriate QCs, such as having more situation dependency, should be

developed. We found clear positive impacts on temperature forecasts for TESTP and TESTT in the

January and August experiments (not shown). The track forecasts of tropical cyclones (TCs) was slightly

improved for both TESTP and TEST when their center positions of 18 cases for 5 TCs were verified

against best track dataset. Finally, we directly compared the impacts between TESTP and TESTT. The

direct comparison of TESTT and TESTP in Fig. 4 shows that relative impacts are mixed and,

interestingly, that TESTT is superior to TESTP up to Day 2 but is inferior beyond Day 2 in the tropics

for both the January and August experiments. We need more investigation to make a firm conclusion

on whether TESTT or TESTP are superior.

Figure 2. Relative forecast error reduction (%) of zonal wind as a function of forecasts up to 120

h for (upper) TESTP and (lower) TESTT for the January experiments. It is verified against PT in

(a, d) the northern hemisphere (20°N–90°N), (b, e) tropics (20°N–20°S), (c, f), and southern

hemisphere (20°S–90°S). Positive values correspond to forecast improvement in TESTs. The

contour lines indicate the statistical significance at the 90% and 95% confidence levels.

(a) TESTP in NH (b) TESTP in TR (c) TESTP in SH

(d) TESTT in NH (e) TESTT in TR (f) TESTT in SH

Figure 3. Similar to Fig. 2 but for the August experiment.

(a) TESTP in NH (b) TESTP in TR (c) TESTP in SH

(d) TESTT in NH (e) TESTT in TR (f) TESTT in SH

Kozo Okamoto 19th Coherent Laser Radar Conference

CLRC 2018, June 18 – 21 4

4. Summary and final comments

We found that a space-based DWL gave us significant positive forecast impacts from the SOSE-based

OSSE study. The similar positive impacts were achieved by DWLs on polar- and tropical-orbiting

satellites and decision of the superiority of DWL on either satellite could not be made at this stage of

the study. We also found positive impacts of DWL in both August and January and greater in January.

Also, the results demonstrated the importance of appropriate QC and observation error assignment and

that suggested more positive impacts would be obtained by refining them. For example, the simple

observation error inflation setting in this study could be replaced with an adaptive inflation according

to the density of existing observations. Furthermore, some observation errors were not taken into

account such as neglecting local effect of vertical wind component. Future study will include these

developments. We will investigate the complementarity between DWL and atmospheric motion vectors

(AMVs) derived from tracking successive imageries of passive infrared and visible imagers [7].

5. Acknowledgements

A part of this research was supported by JSPS KAKENHI under Grant Numbers 15K05293 and

15K06129.

6. References

[1] Okamoto, K., T. Ishibashi, S. Ishii, P. Baron, K. Gamo, T. Y. Tanaka, K. Yamashita, and T. Kubota:

“Feasibility study for future space-borne coherent Doppler wind lidar. part 3: Impact assessment using sensitivity

observing system simulation experiments”, J. Meteor. Soc. Japan, 96, 179-199, (2018).

[2] Marseille, G. J., A. Stoffelen, and J. Barkmeijer: “Sensitivity observing system experiment (SOSE): A new

effective NWP‐based tool in designing the global observing system,” Tellus A, 60, 216–233 (2008).

[3] Tanaka, T. Y., and M. Chiba, “Global simulation of dust aerosol with a chemical transport model,

MASINGAR”, J. Meteor. Soc. Japan, 83A, 255–278 (2005).

[4] Baron, P., S. Ishii, K. Okamoto, K. Gamo, K. Mizutani, C. Takahashi, T. Itabe, T. Iwasaki, T. Kubota, T. Maki,

R. Oki, S. Ochiai, D. Sakaizawa, M. Satoh, Y. Satoh, T. Y. Tanaka, and M. Yasui: “Feasibility study for future

spaceborne coherent Doppler Wind Lidar. Part 2: Measurement simulation algorithms and retrieval error

characterization,” J. Meteor. Soc. Japan, 95, 319–342 (2017).

[5] Ishii, S., K. Mizutani, P. Baron, M. Aoki, K. Mizutani, M. Yasui, S. Ochiai, A. Sato, Y. Satoh, T. Kubota, D.

Sakaizawa, R. Oki, K. Okamoto, T. Ishibashi, T. Y. Tanaka, T. T. Sekiyama, T. Maki, K. Yamashita, T. Nishizawa,

M. Satoh, and T. Iwasaki: “Feasibility study for future spaceborne coherent doppler wind lidar, Part 1: Global

Wind Profile Observing System,” J. Meteor. Soc. Japan, 95, 301–317 (2017) .

[6] Japan Meteorological Agency, Outline of the operational numerical weather prediction at the Japan

Meteorological Agency [Available at http://www.jma.go.jp/jma/jma-eng/jma-center/nwp/outline-nwp/index.htm]

(2007).

[7] Ishii, S., K. Okamoto, P. Baron, T. Kubota, Y. Satoh, D. Sakaizawa, T. Ishibashi, T. Y. Tanaka, K. Yamashita,

S. Ochiai, K. Gamo, M. Yasui, R. Oki, M. Satoh, and T. Iwasaki, “Measurement performance assessment of future

space-borne Doppler wind lidar,” SOLA, 12, 55–59 (2016).

Figure 4. Relative forecast error reduction (%) of zonal wind speed for TESTT against TESTP.

A positive value indicates that TEST has a smaller RMSE than TESTP.

(a) U TR for January exp (b) U TR for August exp