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Hindawi Advances in Meteorology Volume 2018, Article ID 7363194, 12 pages https://doi.org/10.1155/2018/7363194 Research Article A Preliminary Assessment of the Impact of Assimilating Satellite Soil Moisture Data Products on NCEP Global Forecast System 1,2 3 3,4 1 Weizhong Zheng , Xiwu Zhan, Jicheng Liu, and Michael Ek NOAA/NCEP/Environmental Modeling Center, College Park, MD 20740, USA IMSG, NOAA/NCEP/Environmental Modeling Center, College Park, MD 20740, USA NOAA/NESDIS/Center for Satellite Applications and Research, College Park, MD 20740, USA ESSIC/CICS, University of Maryland College Park, College Park, MD 20740, USA Correspondence should be addressed to Weizhong Zheng; weizhong.zheng@noaa.gov Received 22 February 2018; Accepted 8 May 2018; Published 10 June 2018 Academic Editor: Runping Shen Copyright © 2018 Weizhong Zheng et al. (is is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. It is well documented that soil moisture has a strong impact on precipitation forecasts of numerical weather prediction models. Several microwave satellite soil moisture retrieval data products have also been available for applications. However, these observational data products have not been employed in any operational numerical weather or climate prediction models. In this study, a preliminary test of assimilating satellite soil moisture data products from the NOAA-NESDIS Soil Moisture Operational Product System (SMOPS) into the NOAA-NCEP Global Forecast System (GFS) is conducted. Using the ensemble Kalman filter (EnKF) introduced in recent year publications and implemented in the GFS, the multiple satellite blended daily global soil moisture data from SMOPS for the month of April 2012 are assimilated into the GFS. (e forecasts of surface variables, anomaly correlations of isobar heights, and precipitation forecast skills of the GFS with and without the soil moisture data assimilation are assessed. (e surface and deep layer soil moisture estimates of the GFS after the satellite soil moisture assimilation are found to have slightly better agreement with the ground soil moisture measurements at dozens of sites across the continental United States (CONUS). Forecasts of surface humidity and air temperature, 500 hPa height anomaly correlations, and the precipitation forecast skill demonstrated certain level of improvements after the soil moisture assimilation against those without the soil moisture assimilation. However, the methodology for the soil moisture data assimilation into operational GFS runs still requires further development efforts and tests. For the state variable of soil moisture, current GFS opera- 1. Introduction tional runs basically use soil moisture estimates of the Noah Soil moisture is a critical hydrospheric state variable that land surface model (LSM) based on precipitation simula- often limits the exchanges of water and energy between the tions from previous GFS runs. Because of the uncertainties atmosphere and land surface, controls the partitioning of associated with the precipitation simulations and other rainfall among evaporation, infiltration, and runoff, and thus meteorological forcing data for the Noah LSM, the initial may have significant impacts on numerical weather, climate, values used for the GFS runs may not represent the real- and hydrologic predictions. (e Global Forecast System world soil moisture level, which may contribute a certain level of errors of the GFS forecasts. (GFS) of National Centers of Environmental Prediction (NCEP) of NOAA is the primary weather forecast model In the past decade, several sets of global soil moisture that provides up to 16-day weather forecasts for users data products have been generated from satellite observa- around the world. For each GFS run, a set of initial values of tions and have become available for various applications system state variables including soil moisture is required. [1, 2]. (e NASA Soil Moisture Active/Passive (SMAP) 2 Advances in Meteorology in the following section, introduces the implementation of the mission (previously called HYDROS mission) was launched solely for observing global soil moisture [3] in January 2015. EnKF soil moisture data assimilation algorithm in the GFS in Section 3, and examines the impact of assimilating SMOPS A Soil Moisture Operational Product System (SMOPS) has been developed at the National Environmental Satellite, data product on GFS forecasts by validating the GFS model Data, and Information Service (NESDIS) of NOAA to simulations with ground measurements of major system state provide global satellite soil moisture data products primarily variables in Section 4, compares the anomaly correlations, for numerical weather prediction applications at NOAA, bias, and root-mean-square errors of isobar heights in Section NCEP, and for other users [4]. With those soil moisture 5, and demonstrates the precipitation forecast skill scores in data becoming conveniently available, it is a natural next Section 6. Finally, methodology and findings of this study are summarized and discussed in Section 7. step to apply them in the numerical weather prediction models and examine their impacts on numerical weather predictions. 2. Satellite Soil Moisture Data Products In the past years, many studies have explored approaches (e NCEP GFS, North American Mesoscale System (NAM), to assimilating satellite soil moisture observations into land and their associated assimilation systems include a land surface models to improve land surface process simulations and in turn to improve numerical weather predictions [5–13]. surface model (LSM) component that requires soil moisture data as an input for accurate weather and seasonal climate (e EUMETSAT (European Organization for the Exploita- predictions. Currently, soil moisture in the NCEP models is tion of Meteorological Satellites) ASCATsurface soil moisture estimated via the background simulation of the LSM of the product is assimilated into the ECMWF numerical weather assimilation system. (is soil moisture estimates contain prediction system [6, 14]. (ey found that the ASCAT soil considerable biases and uncertainties. In the past decades, moisture nudging scheme improves the model soil moisture several low-frequency microwave satellite sensors have been and screen-level parameters but has a slightly negative impact used to retrieve surface soil moisture with a certain level of on the atmospheric forecasts. (ey also demonstrated success [1]. Satellite-based global soil moisture observational a neutral impact on both soil moisture and screen-level pa- data products are believed to provide a substantial constraint rameter forecasts using ASCAT soil moisture data via an to the model estimate uncertainties and therefore improve extended Kalman filter data assimilation approach. At the United Kingdom Meteorological Office (UKMO), a simple the global and mesoscale model accuracies of weather forecasts. However, these satellite soil moisture data prod- nudging scheme of ASCAT soil moisture data was imple- ucts have not been used by the NCEP numerical weather mented in operations in July 2010 and it shows positive prediction (NWP) models because either their qualities or evaluation results of soil moisture analysis and forecasts their availabilities/formats do not meet the NWP model scores [15]. Using an ensemble Kalman filter, Draper et al. operation requirements, or algorithms for ingesting the soil [16] confirm the potential of satellite-based soil moisture data moisture data products into the NWP models have not been for NWP applications with the combined data assimilation implemented or tested. To meet the NCEP soil moisture data from the active microwave ASCATand the passive microwave needs, NOAA-NESDIS has developed a Soil Moisture Op- AMSR-E satellite instruments. In this study, we attempt to implement the well- erational Product System (SMOPS) to either ingest or re- trieve near-real-time soil moisture data from available documented soil moisture data assimilation approach in the satellite observations and merge them into a single data layer NCEP GFS as we prepare for the future use of the global soil for better spatial and temporal coverage [4]. SMOPS data moisture data products from NASA’s SMAP mission. (e used in this research are soil moisture retrievals from preparation for applying SMAP data products includes the ASCAT on EUMETSAT’s MetOp-A satellite and the Eu- following steps: (1) develop and make operational a soil ropean Space Agency (ESA) Soil Moisture and Ocean Sa- moisture product system that meets all requirements of soil linity (SMOS) satellite. Processing of these data products moisture data needs for NOAA applications, especially in the includes mainly converting their data files from their GFS; (2) implement the Kalman filter data assimilation algo- original format (bufr or hdf) to SMOPS internal binary rithm to ingest satellite soil moisture data products into the format and resampling to 0.25 degree latitude-longitude GFS; (3) examine the impact of assimilating satellite soil moisture data products on GFS forecasts; and (4) make the soil grids. More details of those data products of either the individual satellite sensors or their blend and their quality moisture data assimilation utility in GFS operational. (e assessments are presented by Zhan et al. [4]. global Soil Moisture Operational Product System (SMOPS) has been developed and operational to provide global soil moisture data products ready for use in the GFS from observations of the 3. Soil Moisture Data Assimilation Method in advanced scatterometer (ASCAT) of operational MetOp-A and Global Forecast System MetOp-B satellites of EUMETSAT, the WindSat of Naval Research Lab (NRL), and the Soil Moisture and Ocean Salinity (e NCEP GFS is the operational NCEP global spectral (SMOS) satellite of European Space Agency (ESA). An en- numerical forecast model (and its associated ensemble semble Kalman (EnKF) filter has been implemented in the Kalman filter (EnKF) hybrid data assimilation system GFS to assimilate the SMOPS data products. (is paper providing the initial states) based on the primitive dynamical describes the satellite soil moisture data products provided by equations for fluid dynamics and a suite of parameteriza- NESDIS Soil Moisture Operational Product System (SMOPS) tions for atmospheric physics. (is model had substantial Advances in Meteorology 3 out at 00, 06, 12, and 18 UTC cycles in the GFS-GSI system, upgrades in recent years (http://www.emc.ncep.noaa. gov/GFS). In particular, the Noah land surface model and only at 00 UTC cycle, the GFS is performed for week one forecast (0–192 hrs) to save computation resources. (e (LSM) (Version 2.7.1) replaced the Oregon State University (OSU) LSM to describe the land surface processes [17–20]. SMOPS has used Noah LSM multiple-year grid-wise means (e Noah LSM has four soil layers (10, 30, 60, and 100 cm and standard deviations to scale surface layer soil moisture thick), including updated treatments of frozen soil physics, retrievals from the individual satellite sensors already before infiltration and runoff, snowpack, canopy resistance, ground blending [4], and the blended soil moisture data are assumed heat flux, soil thermal conductivity, direct surface evapo- to have the same climatology as the model simulations of the ration, and green vegetation cover. (e land surface skin Noah LSM used in the GFS. temperature (LST) is derived from the surface energy budget. Momentum roughness lengths over land are pre- scribed for each month based on calculations from the 4. Impact of Soil Moisture Assimilation on GFS vegetation and land use dataset of Dorman and Sellers [21], Surface State Variables but a new formula is used for the thermal roughness lengths [22, 23] which can substantially reduce land surface skin Using the EnKF implemented in the GFS, the global sat- temperature daytime cold bias and low-level warm bias over ellite soil moisture data products from the NESDIS SMOPS arid land areas during warm seasons. A lookup table used in the have been assimilated for the whole month of April and land surface scheme to control minimum canopy resistance early May 2012. (e GFS-GSI system was run starting from and root depth number was updated to reduce excessive 1 April 2012 and continued until 5 May 2012 with (analysis evaporation to improve the cool and moist bias in the near- run) and without (control run) the EnKF assimilation of surface air temperature and moisture fields during the warm the SMOPS blended soil moisture data products. GFS week season. In terms of land surface characteristics, 9 soil texture one forecast (0–192 hrs) was carried out only at the 00 UTC classes [24, 25] and 13 vegetation types [21] are used. Green cycle. Impact of the soil moisture data assimilation on the vegetation fraction (GVF) is obtained with the NESDIS 5-year NWP is then assessed by comparing the surface state (from April 1985 to March 1991 with the year 1988 excluded) variable forecasts, the anomaly correlation of the pressure- Normalized Difference Vegetation Index (NDVI) monthly level height forecasts, and the precipitation forecast skill climatology [26]. Monthly variation of snow-free surface al- scores with and without the assimilation for this more than bedo is derived in reference to Staylor and Wilbur [27], and for one month experiment. snow cases, the albedo is calculated in the Noah LSM. For the surface state variable forecasts, we first check the Longwave emissivity is prescribed to be unity (black-body soil moisture field. Figure 1 gives the comparison of soil emission) for all surface categories. moisture over the CONUS between the SMOPS data product A new hybrid EnKF, three-dimensional variational and the GFS simulations at the first soil layer averaged for the (3DVAR) data assimilation system, GSI, was implemented whole April 2012. (e time average for the SMOPS used in the into the analysis system of GFS called the Global Data As- assimilation is computed from 1 April to 5 May 2012 based on similation System (GDAS). In this system, the background the daily soil moisture product. (e GFS first layer soil error used to project the information in the observations into moisture at 18:00 UTC from 1 April to 5 May 2012 is computed the analysis is created by a combination of a static background for GFS simulations. It is evident that the difference between error and a new background error produced from a lower the SMOPS and the GFS control run is quite large. (e SMOPS resolution (a horizontal resolution of T254) ensemble Kalman data have been scaled before blending according to the GFS filter [28]. (e atmospheric analysis is generated every 6 hours annual climatology [4], as mentioned before. (e soil moisture by the GSI with the GFS previous forecast as the background. from the SMOPS is around 0.2–0.3 g/kg in the east CONUS (is analysis is then used as the initial conditions for GFS and below 0.2 g/kg in the dry west CONUS, except for subsequent forecasts, and the cycle continues. Washington and Oregon states where soil moisture is around To assimilate soil moisture observations into the GFS, 0.2–0.3 g/kg. (e southwest CONUS as well as northern the ensemble Kalman filter (EnKF) is selected and imple- Mexico is particularly drier. (e surface soil moisture is mented. (e EnKF is a Monte Carlo variant of the Kalman below 0.1 g/kg (Figure 1(a)). (e GFS control run shows filter [29] and works sequentially by performing in turn much high moisture over the whole CONUS (Figure 1(b)) a model forecast and a data assimilation update [30]. (e in this month. It is about 0.1 g/kg higher in the eastern EnKF was demonstrated for land data assimilation in syn- regions and southern regions and about 0.2 k/kg higher in thetic studies where it compared well to the weak constraint the northern regions, that is, mountain regions. In the variational “representer” method [8] and favorably to the northeast regions as well as their adjacent of east Canada, extended Kalman filter [9]. Overall, the EnKF is flexible in its the simulated soil moisture is close to the SMOPS. treatment of errors in model dynamics and parameters and (e soil moisture data assimilation can substantially is very suitable for the modestly nonlinear and intermittent adjust the soil moisture in the GFS model. (e difference of character of land surface processes. the top layer soil moisture between the sensitivity and In this study, considering that soil moisture variation control runs shows that the large impact occurs around the within nonraining days is small and that the blended soil Mississippi River Basin, and in large part of the western US, moisture data from the SMOPS represent only daily soil particularly over the mountain areas where the soil moisture moisture level, the soil moisture data assimilation is carried reduced up to 0.2–0.25 g/kg (Figure 1(c)). As expected, it 4 Advances in Meteorology SMOPS_BL: SOILM 1 (fraction) Ave. 1 Apr–5 May 2012 GFS_CTL: SOILM 1 (fraction) Ave. 1 Apr–5 May 2012 50N 50N 40N 40N 30N 30N 120W 110W 100W 90W 80W 70W 120W 110W 100W 90W 80W 70W 0 0.01 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0 0.01 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 (a) (b) Diff: SOILM 1 (fraction) Ave. 1 Apr–5 May 2012 50N 40N 30N 120W 110W 100W 90W 80W 70W –0.3 –0.25 –0.2 –0.15 –0.1 –0.05 0.05 0.1 (c) Figure 1: ‡e –rst layer (0–10 cm) soil moisture (fractional) over the CONUS at 18:00 UTC, averaged temporally over the period of 1 April to 5 May 2012 from the SMOPS blended product (a), the GFS-CTL (b), and the dierence of CTL and EnKF (c). Table 1: Top layer soil moisture (fractional) validation with USDA-SCAN ground measurements during a period from 1 to 30 April 2012. East CONUS (26 sites) West CONUS (25 sites) Whole CONUS RMSE Bias Correlation coe“cient RMSE Bias Correlation coe“cient RMSE Bias Correlation coe“cient CTL 0.135 0.046 0.565 0.124 0.033 0.448 0.129 0.040 0.508 EnKF 0.130 −0.031 0.613 0.114 −0.021 0.549 0.123 −0.026 0.587 SMOPS 0.133 −0.055 0.601 0.098 −0.036 0.402 0.117 −0.048 0.524 ‡ere are 26 sites for ground measurements over the east CONUS and 25 sites over the west CONUS. Unit: kg/kg. does not show much dierence in northeast US regions as comparing to the USDA-SCAN observation, particularly over well as its adjacent regions of east Canada. the west CONUS. ‡e GFS control run shows too wet over the To examine the improvement of soil moisture simulations, whole CONUS. ‡e GFS-EnKF run corrects the wet bias of we use the ground measurements from the U.S. Department of the GFS control run but shows a little drier comparing to the Agriculture (USDA) Soil Climate Analysis Network (SCAN) USDA-SCAN ground measurements. ‡e table also indicates that the GFS-EnKF run reduces the RMSE and increases the [31], which is the independent observation of soil moisture and to validate the soil moisture estimates. After quality control correlation coe“cient between the model and the ground steps, 26 and 25 sites of the SCAN network are selected over the measurements over both the east and west CONUS, even it has eastern and the western CONUS, respectively. ‡eir corre- a better performance than the SMOPS data, except the RMSE sponding estimates of GFS with and without the data assim- over the east CONUS where the GFS-EnKF run has slightly ilation as well as SMOPS retrievals are compared with the higher RMSE than SMOPS data. It should be noted that the ground measurements of these sites. ‡e comparison statistics comparison statistics did not consider the scale dierences are listed in Table 1. ‡e SMOPS biases are negative both over between the ground measurements and the model estimates or the east and west CONUS, indicating the SMOPS is drier satellite retrievals. Advances in Meteorology 5 RH SFC, CONUS west, 00Z cycle, RH SFC, CONUS east, 00Z cycle, 02 April 2012–05 May 2012 mean 02 April 2012–05 May 2012 mean 30 0 0 –0.3 Difference with respect to PREMKFCTL –0.3 –0.6 –0.6 –0.9 –0.9 –1.2 –1.2 –1.5 –1.5 –1.8 –1.6 –2.1 –2.1 –2.4 –2.4 –2.7 –2.7 Differences outside of outline bars –3 –3 are significant at the 95% confidence level –3.3 0 12 24 36 48 60 72 84 96 108 120 132 144 156 0 12 24 36 48 60 72 84 96 108 120 132 144 156 Forecast hour Forecast hour Obs Obs PREMKFCTL PREMKFCTL PREMKF01 PREMKF01 (a) (b) T SFC, CONUS west, 00Z cycle, T SFC, CONUS east, 00Z cycle, 02 April 2012–05 May 2012 mean 02 April 2012–05 May 2012 mean 11 12 0.7 0.9 0.6 Difference with respect to PREMKFCTL 0.6 Difference with respect to PREMKFCTL 0.5 0.4 0.5 0.3 0.4 0.2 0.1 0.3 0.2 –0.1 0.1 –0.2 –0.3 –0.4 –0.1 –0.5 –0.6 –0.2 Differences outside of outline bars Differences outside of outline bars –0.7 –0.3 –0.8 are significant at the 95% confidence level are significant at the 95% confidence level –0.9 –0.4 0 12 24 36 48 60 72 84 96 108 120 132 144 156 0 12 24 36 48 60 72 84 96 108 120 132 144 156 Forecast hour Forecast hour Obs Obs PREMKFCTL PREMKFCTL PREMKF01 PREMKF01 (c) (d) Figure 2: (a) (Top) mean 7-day diurnal cycle of 2 m relative humidity (%) averaged both spatially over the west CONUS region and temporally over the period of 2 April to 5 May 2012, for 7-day GFS forecast from CTL (red) and EnKF (green); (bottom) as in (top) but shows dierence of CTL and EnKF, plus the results of a statistical Student’s t-test/signi–cance test. ‡e dierences outside of the hollow bars attains the 95% con–dence level based on Student’s t-test. (b) As in (a), but for the east CONUS region. (c) As in (a), but for 2 m air temperature. (d) As in (b), but for 2 m air temperature. 6 Advances in Meteorology Q (g/kg) bias over CONUS: fit to RAOBS Q (g/kg) RMSE over CONUS: fit to RAOBS 00Z cycle 120 hr Fcst, 02 April 2012–05 May 2012 mean 00Z cycle 120 hr Fcst, 02 April 2012–05 May 2012 mean 400 400 700 700 0 0.6 1.2 1.8 2.4 3 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0 0.3 0.6 0.9 1.2 1.5 1.8 2.1 2.4 0 0.6 1.2 1.8 2.4 3 Q bias Obs count (×1000) Q RMSE Obs count (×1000) PREMKFCTL PREMKFCTL PREMKF01 PREMKF01 (a) (b) Figure 3: Speci–c humidity bias (a) and RMSE (b) over the CONUS with validation of sounding data, respectively, (unit: g/kg). Black lines are the GFS-CTL, and red lines are the GFS-EnKF. ‡e number of observation sounding sites is also shown. ‡e improvement of soil moisture can directly aect the validate the speci–c humidity and temperature simulation. near surface forecast of atmospheric humidity and tem- Figure 3 gives a comparison of the vertical pro–les of speci–c perature. ‡is can be seen from the one-month mean humidity bias and RMSE over the CONUS. ‡e bias of forecast (from 2 April to 5 May 2012) of the surface relative speci–c humidity from GFS-EnKF reduced much in the humidity and temperature at 2 meters, averaged over the lower troposphere, from 500 hPa to the surface, where its west CONUS and east CONUS, respectively (Figure 2). RMSE also got some reduction. ‡e moisture in the GFS is Daytime surface relative humidity in the west CONUS in too high near the surface from 850 hPa to the surface, though both GFS runs is lower than the observation during 7-day the GFS-EnKF run reduced this somewhat. ‡e impacts of forecast, and close to the observation in nighttime, though this high bias of moisture on other aspects such as the land the GFS sensitivity run shows a little drier. ‡e surface surface model and the PBL scheme should be investigated. Figure 4 presents a comparison of the vertical pro–les of temperature over the west CONUS is in good agreement with the observation, and the GFS sensitivity run does not temperature bias and RMSE over the CONUS with validation have a big impact (Figure 2(c)). Over the east CONUS, of sounding data. ‡e GFS control run shows cold bias near surface humidity in the GFS control run clearly shows the top of troposphere and warm bias in the middle and lower positive bias in daytime and nighttime, which is consistent troposphere but a little cold near the surface. ‡e GFS-EnKF with the horizontal surface moisture distribution as shown run does not change temperature bias in the high levels, but in Figure 1. ‡is bias got reduced in the GFS sensitivity run, there are some impacts from 500 hPa to the surface. It in- but forecast surface humidity in nighttime is still higher than creases the warm bias but shows a better improvement near the observation. ‡e surface temperature in the GFS run the surface. In the whole troposphere, the GFS-EnKF run got shows a cold bias of daytime after 4 days of forecast, and the a reduction of RMSE, as shown in Figure 4(b). cold bias becomes more obvious with forecast time. ‡is cold After we examined the model performance of surface bias got reduced in the sensitivity run (Figure 2(d)), in- –elds and vertical pro–les compared to the observations as dicating that the soil moisture data assimilation can have shown in the above sections, we further check the model a good improvement in the surface temperature forecast. forecasting by calculating global anomaly correlation (AC) of geopotential heights at 500 hPa for day 5 as well as its bias and RMSE against the model analyses (GDAS). Figure 5 5. Impact of Soil Moisture Assimilation on gives a comparison of the global AC scores for both runs, GFS hPa Height Forecasts and GFS-EnKF shows a positive impact of AC with increase With the improvement of surface –eld simulations from the of 0.003 for the global region. ‡is improvement shows soil moisture data assimilation, there should be impacts of a clear impact after three weeks. the planetary boundary layer (PBL) and lower troposphere. ‡e error reduction of global mean bias and RMSE for In order to examine the impacts, we use the sounding data to geopotential heights at 850 hPa and 500 hPa illustrates the Pressure (hPa) Pressure (hPa) Advances in Meteorology 7 T (K) bias over CONUS: fit to RAOBS T (K) RMSE over CONUS: fit to RAOBS 00Z cycle 120 hr Fcst, 02 April 2012–05 May 2012 mean 00Z cycle 120 hr Fcst, 02 April 2012–05 May 2012 mean 50 50 200 200 400 400 500 500 –2 –1.5 –1 –0.5 0 0.5 1 0 0.6 1.2 1.8 2.4 3 0 0.5 1 1.5 2 2.5 3 3.5 0 0.6 1.2 1.8 2.4 3 T Bias Obs count (×1000) T RMSE Obs count (×1000) PREMKFCTL PREMKFCTL PREMKF01 PREMKF01 (a) (b) Figure 4: Temperature bias and RMSE over the CONUS with validation of sounding data, respectively (unit: K). Black lines are the GFS- CTL, and red lines are the GFS-EnKF. ‡e number of observation sounding sites is also shown. improvement of the forecasts (Figure 6). ‡e GFS control Anomaly correlation: HGT P500 G2 00Z, fh 120 run shows a negative bias and becomes larger during a seven-day forecast. ‡e GFS-EnKF reduced its bias, and 0.9 this improvement shows signi–cance at the 95% con–dence level. ‡e RMSE analysis shows some improvement from 0.8 GFS-EnKF run but not up to the signi–cant level. 0.7 ‡us, the assimilation of soil moisture can reduce errors of surface temperature and surface humidity and errors of 0.6 the vertical temperature and humidity pro–les, modify the boundary layer structure and atmospheric stability, and 0.5 –nally have signi–cant impacts on the high-level heights as 6 APR 11 APR 16 APR 21 APR 26 APR 1 MAY well as the precipitation processes as discussed in Section 6. Verification date PREMKFCTL 0.840 29 6. Quantitative Precipitation Forecasts PREMKF01 0.843 29 More investigation related to the impact of soil moisture Figure 5: Analysis on global anomaly correlation (AC) at 500 hPa data assimilation can be done concerning precipitation for day 5 from GFS-CTL (black) and GFS-EnKF (red). forecasting in the model. A quantitative precipitation esti- mate (QPF) is used for evaluation of the GFS model per- formance. ‡e precipitation observation estimates come (day 2) and from 60 h to 84 h (day 3), separately. ‡e ETS from the Climate Prediction Center’s (CPC) gauge obser- from the GFS-EnKF is slightly higher than that from the GFS- vation over the CONUS, which is usually used in the NCEP CTL for the light or heavy precipitation amounts, and bias global NWP Model Deterministic Forecast Veri–cation reduction in GFS-EnKF versus GFS-CTL is quite consistent Package applying the Monte Carlo signi–cance test rather from light to heavy precipitation and signi–cant at the 95% than Student’s t-test applied in Figures 2 and 6 because the con–dence level for light and medium precipitation amounts. conventional method of signi–cance tests such as Student’s ‡ese results indicate that the soil moisture data assimilation t-test is not applicable for precipitation skill scores (http:// has a positive impact of precipitation forecasting. www.emc.ncep.noaa.gov/gmb/STATS_vsdb/) [32]. In terms of day 3 precipitation forecasting, Figure 8 in- ‡e equitable threat scores (ETS) and bias scores [33] of dicates that the GFS-EnKF yields higher equitable threat scores precipitation over the CONUS for a period from 2 April to 5 than the CTL, and this skill dierence attains the 95% con–- May 5 2012 are calculated with the CPC observation data. dence level for the majority of the light and medium pre- Figures 7 and 8 illustrate the GFS forecast from 36 h to 60 h cipitation amounts but also shows a big drop of ETS for the Pressure (hPa) Pressure (hPa) 8 Advances in Meteorology HGT: bias HGT: bias P500 G2 00Z. 02 April 2012–05 May 2012 mean P850 G2 00Z. 02 April 2012–05 May 2012 mean 0.4 0.2 0.1 0.2 0 –0.1 –0.2 –0.2 –0.3 –0.4 –0.4 –0.5 –0.6 –0.6 –0.8 –0.7 –0.8 –1 –0.9 –1.2 –1 –1.1 –1.4 –1.2 –1.6 –1.3 –1.4 –1.8 –1.5 –2 –0.16 0.48 Difference with respect to PREMKFCTL Difference with respect to PREMKFCTL 0.16 0.42 0.14 0.36 0.12 0.3 0.1 0.24 0.08 0.18 0.06 0.12 0.04 0.02 0.06 Bias differences outside of outline bars Bias differences outside of outline bars –0.02 –0.06 are significant at the 95% confidence level are significant at the 95% confidence level –0.04 04896 144 0 48 96 144 Forecast hour Forecast hour PREMKFCTL 30 PREMKFCTL 30 PREMKF01 30 PREMKF01 30 (a) (b) HGT: RMSE HGT: RMSE P500 G2 00Z. 02 April 2012–05 May 2012 mean P850 G2 00Z. 02 April 2012–05 May 2012 mean 75 55 67.5 49.5 60 44 52.5 38.5 45 33 37.5 27.5 30 22 22.5 16.5 15 11 7.5 5.5 0 0.0 0.06 Difference with respect to PREMKFCTL Difference with respect to PREMKFCTL 0.05 –0.06 –0.05 –0.12 –0.1 –0.18 –0.15 –0.24 –0.2 –0.3 –0.25 –0.36 –0.3 –0.42 –0.35 RMS differences outside of outline bars RMS differences outside of outline bars –0.48 –0.4 are significant at the 95% confidence level are significant at the 95% confidence level –0.54 –0.45 0 48 96 144 0 48 96 144 Forecast hour Forecast hour PREMKFCTL 30 PREMKFCTL 30 PREMKF01 30 PREMKF01 30 (c) (d) Figure 6: (Top) mean 7-day bias of geopotential heights at 500 hPa (a) and 850 (b) averaged both spatially over the global region and temporally over the period of 2 April to5 May 2012, for 7-day GFS forecast from CTL (black) and EnKF (red); (bottom) as in (top) but shows dierence of CTL and EnKF, plus the results of a statistical Student’s t-test/signi–cance test. ‡e dierences outside of the hollow bars attain the 95% con–dence level based on Student’s t-tests. Corresponding RMSE of CTL (black) and EnKF (red) at 500 hPa (c) and 850 hPa (d) for the same period as in (a) or (b). Advances in Meteorology 9 CONUS precip skill scores, f36–f60, 02 April 2012–05 May 2012 00Z cycle 0.4 0.3 1.5 0.2 0.1 23409 13905 8456 4828 2289 789 289 48 0 23409 13905 8456 4828 2289 789 289 48 0 0 0.5 0.6 Difference with respect to PREMKFCTL Difference with respect to PREMKFCTL 0.06 0.3 0.03 0 0 –0.03 –0.3 –0.06 –0.6 0.2 2 5 10 15 25 35 60 75 0.2 2 5 10 15 25 35 60 75 reshold (mm/24 hr) reshold (mm/24 hr) Differences outside of the hollow bars are 95% significant based on 10,000 Monte Carlo tests PREMKFCTL PREMKFCTL PREMKF01 PREMKF01 Figure 7: Precipitation equitable threat scores (left) and bias scores of the GFS precipitation forecast from 36 h to 60 h over the CONUS for CTL (black) and EnKF (red), as temporally averaged over the period of 2 April to 5 May 2012. ‡e black numbers denote number of observation stations, and the lower panels indicate their dierences with the Monte Carlo signi–cance tests. ‡e dierences outside of the hollow bars attain the 95% con–dence level based on 10,000 Monte Carlo tests. the results, it may be concluded that assimilating satellite soil heavy precipitation range. It is noted that the station number of observed heavy precipitation is smaller, so the scores calculated moisture data products may have certain positive impacts on could be not as accurate as ones for light or medium pre- improving the estimates of surface and deeper layer soil cipitation. Similar to day 2, the bias score comparison between moisture, surface humidity, and air temperature and in- two runs shows the GFS-EnKF gives a substantial reduction of creasing anomaly correlations of isobar heights. ‡e sta- precipitation bias for all the precipitation amount range with the tistically signi–cant impacts on the skill of GFS forecasts for great con–dence. lower precipitation amounts over the CONUS are also notable, especially for the reduction of precipitation bias. However, several issues need to be addressed before the 7. Conclusion and Discussion GFS model could operationally ingest satellite soil moisture It is well documented that soil moisture has a strong impact observations. Firstly, microwave satellite soil moisture re- trievals represent the soil moisture levels of various soil on precipitation forecasts of numerical weather prediction models [34]. Several microwave satellite soil moisture re- depths while the GFS model surface layer is always 10 cm. trieval data products have also been available for applica- C/X-band sensors (such as ASCAT, AMSR-E, WindSat, and tions [2]. However, it has not been demonstrated how these the Advanced Microwave Scanning Radiometer on JAXA’s satellite soil moisture data products could improve nu- GCOM-W1 satellite, AMSR2) have a typical sensing depth merical weather or seasonal climate predictions. A pre- of 1-2 cm. ‡e sensing depth varies with top layer soil liminary test of assimilating NOAA-NESDIS SMOPS soil moisture level too: the depth is shallower when the top layer moisture data products into NOAA-NCEP Global Forecast soil is wetter. ‡e sensing depth of L-band sensors System is conducted in this study. From the above analysis of (e.g., SMOS of ESA and future SMAP of NASA) could be Equitable threat score BIAS score 10 Advances in Meteorology CONUS precip skill scores, f60–f84, 02 April 2012–05 May 2012 00Z cycle 0.3 1.5 0.2 0.1 23409 13905 8456 4828 2289 789 289 48 0 23409 13905 8456 4828 2289 789 289 48 0 0.5 0.16 Difference with respect to PREMKFCTL 0.6 Difference with respect to PREMKFCTL 0.08 0.3 0 0 –0.3 –0.08 –0.6 –0.16 0.2 2 5 10 15 25 35 60 75 0.2 2 5 10 15 25 35 60 75 reshold (mm/24 hr.) reshold (mm/24 hr.) Differences outside of the hollow bars are 95% significant based on 10,000 Monte Carlo Tests PREMKFCTL PREMKFCTL PREMKF01 PREMKF01 Figure 8: Precipitation equitable threat scores (left) and bias scores of the GFS forecast from 60 h to 84 h over the CONUS for CTL (black) and EnKF (red), as temporally averaged over the period of 2 April to 5 May 2012. ‡e black numbers denote number of observation stations, and the lower panels indicate their dierences with the Monte Carlo signi–cance tests. ‡e dierences outside of the hollow bars attain the 95% con–dence level based on 10,000 Monte Carlo tests. to routinely or operationally carry out this process in oper- 5 cm. In this study, the satellite observations are assumed to represent the top 10 cm soil moisture layer of GFS. ‡e ational GFS runs still requires further developments and tests. validity of this assumption is still unknown. Data Availability Secondly, the EnKF data assimilation algorithm requires that both the observational soil moisture data and the GFS ‡e data used to support the –ndings of this study are soil moisture simulations have a Gaussian distribution and available from the corresponding author upon request. no bias from each other. ‡is study used their multiyear means and standard deviations to make the satellite retrieval Conflicts of Interest climatology match the GFS simulations. ‡e impact study ‡e authors declare that they have no con®icts of interest. was carried out for only about one month (April 2012) because of the GFS computing resource limitation. Results in Acknowledgments Figure 1 indicate that satellite soil moisture retrievals are drier than GFS simulations for the month. Whether the ‡e authors would like to thank our many collaborators or result of this impact study is in®uenced by the seasonality partners at NCEP/EMC, NESDIS, and JCSDA for their dierence between the satellite retrievals and the GFS model useful suggestions and bene–cial comments. Internal re- simulations needs further investigation. views from Youlong Xia and Jiarui Dong at NCEP/EMC are ‡irdly, the optimal EnKF data assimilation result re- acknowledged. quires the model and observation error covariances to be determined correctly. We managed to make the normalized References EnKF innovation statistics meet the optimal requirements by empirically adjusting the model and observation error co- [1] E. Njoku, T. Jackson, V. Lakshmi, T. Chan, and S. Nghiem, variance level according to Crow and Van Loon [35]. 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