An application of the multi-physical method of determining error of WRF models to simulate the track and intensity Usagi storm in 2018

This paper presents the test results of the WRF model error determination methods simulating the trajectory and intensity of storm Usagi in 2018. The study conducted three experiments: (1) The Combination of 11 options physical model, 21 composites, no increase in error correlation (MP); (2) Using a set of physical model, 21 composite components, multiplier growth factor l = 6.5 (MI); (3) Using a set of physical model, 21 compositions, no increase in error correlation (PF). Test results show that the multi-physics (MP) method has quite well simulated the intensity as well as the moving direction of the northern cold high pressure in the active Usagi storm area. As a result, The 2018 - Usagi 's trajectory and intensity is simulated in MP test better than in MI test and PF test. Specifically, at the 48-hour forecast term, the orbital prediction error of the MP test is below 350 km which is lower than the two tests (MI and PF), The orbital error in the MP test at the forecast term of 60 and 72 hours is 3-6% reduction in compared with the PF test, and in compared with the MI test, the orbital predictive error of the MP test decreased from 5% to 10% at the 12 hour to 72 hours forecast period. In terms of intensity, absolute error of Pmin (Vmax) in MP test always has lower value than two MI and PF tests. In particular, the absolute error of Vmax in the MP test decreased from 30-40% in compared to the other two trials at all forecasting terms, especially at the forecast term longer than 2 days. Thus, the multi-physics method can be a potential application of determining the error for the model to simulate the trajectory and intensity of storms affecting Vietnam.

Including the spatial observation error correlation in data assimilation of AMSU-A radiances

<p>Recent developments in sensing technology increased the number of observations both in space and time. It is essential to effectively utilize the information from observations to improve numerical weather prediction (NWP). It is known to have correlated errors in observations measured with a single instrument, such as satellite radiances. The observations with the horizontal error correlation are usually thinned to compensate for neglecting the error correlation in data assimilation. This study explores to explicitly include the horizontal observation error correlation of Advanced Microwave Sounding Unit-A (AMSU-A) radiances using a global atmospheric data assimilation system NICAM-LETKF, which comprises the Nonhydrostatic ICosahedral Atmospheric Model (NICAM) and the Local Ensemble Transform Kalman Filter (LETKF). This study performs the data assimilation experiments at 112-km horizontal resolution and 38 vertical layers up to 40 km and with 32 ensemble members.</p><p>In this study, we estimate the horizontal observation error correlation of AMSU-A radiances using innovation statistics. The computation cost of inverting the observation error covariance matrix will increase when non-zero off-diagonal terms are included. In this study, we assume uncorrelated observation errors between different instruments and observation variables, so that the observation error covariance matrix becomes block diagonal with only horizontal error correlations included. The computation time of the entire LETKF analysis procedure is increased only by up to 10 % compared with the case using the diagonal observation error covariance matrix. The analyses and forecasts of temperature and zonal wind in the mid- and upper-troposphere are improved by including the horizontal error correlations. We will present the most recent results at the workshop.</p>

A new exponentially-decaying error correlation model for assimilating OCO-2 column-average CO₂ data, using a length scale computed from airborne lidar measurements

To check the accuracy of column-average dry air CO2 mole fractions (XCO2) retrieved from Orbiting Carbon Overvatory (OCO-2) data, a similar quantity has been measured from the Multi-functional Fiber Laser Lidar (MFLL) aboard aircraft flying underneath OCO-2 as part of the Atmospheric Carbon and Transport (ACT)-America flight campaigns. Here we do a lagged correlation analysis of these MFLL-OCO-2 column CO2 differences and find that their correlation spectrum falls off rapidly at along-track separation distances of under 10 km, with a correlation length scale of about 10 km, and less rapidly at longer separation distances, with a correlation length scale of about 20 km. The OCO-2 satellite takes many CO2 measurements with small (~3 km2) fields of view (FOVs) in a thin (<10 km wide) swath running parallel to its orbit: up to 24 separate FOVs may be obtained per second (across a ~6.75 km distance on the ground), though clouds, aerosols, and other factors cause considerable data dropout. Errors in the CO2 retrieval method have long been thought to be correlated at these fine scales, and methods to account for these when assimilating these data into top-down atmospheric CO2 flux inversions have been developed. A common approach has been to average the data at coarser scales (e.g., in 10-second-long bins) along-track, then assign an uncertainty to the averaged value that accounts for the error correlations. Here we outline the methods used up to now for computing these 10-second averages and their uncertainties, including the constant-correlation-with-distance error model currently being used to summarize the OCO-2 version 9 XCO2 retrievals as part of the OCO-2 flux inversion model intercomparison project. We then derive a new one-dimensional error model using correlations that decay exponentially with separation distance, apply this model to the OCO-2 data using the correlation length scales derived from the MFLL-OCO-2 differences, and compare the results (for both the average and its uncertainty) to those given by the current constant-correlation error model. To implement this new model, the data are averaged first across 2-second spans, to collapse the cross-track distribution of the real data onto the 1-D path assumed by the new model. A small percentage of the data that cause nonphysical negative averaging weights in the model are thrown out. The correlation lengths over the ocean, which the land-based MFLL data do not clarify, are assumed to be twice those over the land. The new correlation model gives 10-second XCO2 averages that are only a few tenths of a ppm different from the constant-correlation model. Over land, the uncertainties in the mean are also similar, suggesting that the +0.3 constant correlation coefficient currently used in the model there is accurate. Over the oceans, the twice-the-land correlation lengths that we assume here result in a significantly lower uncertainty on the mean than the +0.6 constant correlation currently gives – measurements similar to the MFLL ones are needed over the oceans to do better. Finally, we show how our 1-D exponential error correlation model may be used to account for correlations in those inversion methods that choose to assimilate each XCO2 retrieval individually, and to account for correlations between separate 10-second averages when these are assimilated instead.