H. LIU AND X. ZOU AUGUST 2001 LIU AND ZOU. The Florida State University, Tallahassee, Florida

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1 AUGUST 2001 LIU AND ZOU 1987 The Impact of NORPEX Targeted Dropsondes on the Analysis and 2 3-Day Forecasts of a Landfalling Pacific Winter Storm Using NCEP 3DVAR and 4DVAR Systems H. LIU AND X. ZOU The Florida State University, Tallahassee, Florida (Manuscript received 3 February 2000, in final form 12 February 2001) ABSTRACT During the North Pacific Experiment (NORPEX), both the Navy Operational Global Atmospheric Prediction System and the National Centers for Environmental Prediction (NCEP) operational forecast systems found a 48- h forecast degradation over the NORPEX forecast verification region due to the inclusion of a set of NORPEX targeted dropsondes deployed north of Hawaii during January The NCEP three- and four-dimensional varitional data assimilation (3DVAR and 4DVAR) systems are used here to reassess the impact of these dropsonde observations on model predictions. The assimilation of these targeted dropsondes excluding the conventional observations improved the 48-h forecast over the NORPEX forecast verification region. However, the addition of the dropsonde data to an analysis that already contained various conventional observations degraded the 48- h forecast over the NORPEX forecast verification region. In the later case, the dropsonde data still improved and had its largest impact on the forecast over the northeast Pacific (outside of the forecast verification region). In this region, errors in the forecast using only conventional observations were largest. Furthermore, assimilation of the targeted dropsonde data using the 4DVAR approach produced greater improvements in the 1 3-day forecasts over the Pacific Ocean than the 3DVAR approach did in both cases, with and without conventional observations. 1. Introduction Numerical weather prediction (NWP) models are used for forecasting guidance. However, an analysis of current weather (i.e., the model initial conditions) with reasonably good accuracy is required for a NWP model to make a useful prediction. The current analysis of weather is usually obtained through a data assimilation procedure that combines the observations with a priori knowledge of the evolving atmospheric state given by a numerical forecasting model or an independent analysis (Daley 1991). Due to fewer observations at sea than over land, the 2 3-day forecasts for oceanic storms that approach the West Coast is still a challenge. These powerful winter storms often cause serious damage. The major field program, the North Pacific Experiment (NORPEX, 16 Jan 31 Feb 1998), was developed to improve short-range forecasts of winter-storms that move eastward across the North Pacific Ocean and make landfall in western North America (Langland et al. 1999). A central hypothesis of the NORPEX effort was that it may be worthwhile to add in situ observations in particular areas where analysis errors are large and/ or expected to grow rapidly, as opposed to areas from Corresponding author address: Dr. X. Zou, Department of Meteorology, The Florida State University, 404 Love Bldg., Tallahassee, FL zou@met.fsu.edu where analysis errors do not develop into large forecast errors (Langland et al. 1999). The prime spots for enhanced observation, all based on objective model-dependent guidances such as adjoint singular vector (SV; Gelaro et al. 1998) and ensemble transform (ET; Bishop and Toth 1999) methods, include developing cyclones as well as their precursors farther upstream. Many targeted dropsonde observations were made during the experiment based on adjoint SV and ET results for selected forecast verification regions (Langland et al. 1999). The effects of targeted dropsonde observations were evaluated for several field experiments such as the Fronts and Atlantic Storm-Track Experiment (Szunyogh et al. 1999a), NORPEX (Szunyogh et al. 1999b), and the 1999 Winter Storm Reconnaissance Program (Szunyogh et al. 2000). These studies indicated that (i) the targeted dropsondes provide useful information for capturing features of precursors that may trigger cyclone development, (ii) the targeted dropsondes improve numerical model forecasts with a 10% 20% rms error reduction, and (iii) though the overall impact of the targeted data on forecast quality is positive, there were cases when the extra data degraded the forecasts. Questions that will be further examined in this study are, (a) How does the forecast impact of dropsondes depend on data assimilation methods? and (b) what causes a forecast degradation? The impact of a particular dataset on a numerical forecast depends very much on how the information 2001 American Meteorological Society

2 1988 MONTHLY WEATHER REVIEW VOLUME 129 FIG. 1. The 300-hPa wind speeds and vectors at 0000 UTC 29 Jan Contour interval is 10 m s 1. contained in these data is extracted and incorporated into the initial condition of that forecast. It is possible that either the useful information in the targeted observations is not extracted properly, or the main signal of the dropsonde observations does not propagate into the designated forecast verification region. Therefore, a good set of data may result in a forecast degradation over the forecast verification region that was used for targeting (Szunyogh et al. 1999a). During NORPEX, approximately 700 targeted tropospheric soundings of temperature, wind, moisture, and pressure were obtained using aircraft in 38 storm reconnaissance missions from near flight level (typically ft) to the sea surface, primarily between Hawaii and Alaska. Preliminary results of NORPEX data impact studies using the U.S. Navy and National Weather Service forecast models showed an approximate 10% reduction in the mean 2-day forecast errors over the NORPEX forecast verification region (NOR- PEX.FVR) from the inclusion of targeted dropsonde data in the operational data assimilation system (Langland et al. 1999; Szunyogh et al. 1999b). Analyses were used for forecast verification in these studies. However, a few 48-h forecasts were found to have been degraded by the addition of the targeted dropsonde data. One particular case was the 48-h forecast initialized at 0000 UTC 30 January 1998, for which the 48-h forecast with dropsonde data collected in roughly 8-h intervals centered around 0000 UTC 29 January and 0000 UTC 30 January 1998 was worse than that without the dropsonde data (see Fig. 10 in Langland et al. 1999). The National Centers for Environmental Prediction (NCEP) operational results also showed a degradation in the 48-h forecast of this case due to the use of targeted dropsondes (Toth et al. 1999a). The rms errors (verified with radiosonde data over NORPEX.FVR) of the 48-h forecast starting from the three-dimensional variation data assimilation (3DVAR) analyses with and without dropsonde data were 3.1 and 2.8 hpa for the surface pressure, and 9.7 and 8.7 m s 1 for the wind, respectively (Z. Toth 2000, personal communication). In order to gain some insights into (i) the role of data assimilation methods in determining the forecasting impact of targeted dropsonde data, and (ii) the characteristics of the propagation of fast-growing errors that caused forecast improvement or degradation, we conducted several 3DVAR and 4DVAR experiments using the NCEP global spectral model for this particular Pacific winter storm case. The 3DVAR/4DVAR with and without dropsondes are conducted in both the absence and the presence of conventional data, which allows us to examine the differences in the analyses and the subsequent 2 3-day forecasts that result from the use of different data as- FIG. 2. Locations of targeted dropsondes during 6-h time intervals centered at (a) 0000 UTC 29 Jan, (b) 0000 UTC 30 Jan, and (c) 0000 UTC 31 January ( ), 0000 UTC 1 Feb ( ), and 0000 UTC 2 Feb ( ) The numbers in (a) and (b) represent times (h) away from 0000 UTC at various dates. The total number of conventional observational data, available during 3 h of 0000 UTC 31 January, in each model grid box of about is shown for (d) wind and (e) temperature.

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5 AUGUST 2001 LIU AND ZOU 1991 TABLE 1. Main features of various experiments and their naming convention for the evaluation of the impact of targeted dropsonde data during NORPEX. Expt Observations Assimilation method Initial condition Forecast period (h) CTRL CONV3 CONV4 BOTH3 DROP3 BOTH4 DROP4 Conventional Conventional Dropsonde, conventional Dropsonde Dropsonde, conventional Dropsonde 3DVAR 4DVAR 3DVAR 3DVAR 4DVAR 4DVAR Guess at 0000 UTC 29 Jan Analysis at 0000 UTC 30 Jan Analysis at 0000 UTC 30 Jan Analysis at 0000 UTC 30 Jan Analysis at 0000 UTC 30 Jan Analysis at 0000 UTC 30 Jan Analysis at 0000 UTC 30 Jan similation approaches (3DVAR vs 4DVAR) and from the use of single and multiple types of observations. 2. Case description and experiment design a. The winter storm that occurred between 29 January and 2 February 1998 on the west coast of the United States The winter storm at the end of January and the beginning of February 1998 on the west coast of the United States occurred when El Niño reached its peak intensity over California. The synoptic regime during this period was characterized by a strong upper-level jet. On 29 January 1998, the jet speed exceeded 90 m s 1 at 300 hpa north of Hawaii, based on the NCEP largescale analysis (Fig. 1). Nearly 100 dropsonde profiles were obtained during two roughly 8-h intervals centered around 0000 UTC 29 January and 0000U TC 30 January 1998 along the flight track of a single aircraft on 29 January (Fig. 2a) and three flight tracks on 30 January 1998 (Fig. 2b). The 29 January flight used Navy Research Laboratory (NRL) SV guidance and used one aircraft from Hawaii. The 30 January flights followed NCEP ET guidance and used three aircraft (one from Hawaii and two from Alaska). The horizontal spacing of these dropsondes was about km. The dropsondes on 29 January were clustered downstream of a midlatitude 500-hPa trough over two surface low pressures centers whose central sea level pressures (SLPs) were 970 hpa (near 40 N, 165 W) and 975 hpa (near 50 N, 150 W), respectively (Fig. 3a). The low to the northeast was decaying as the one to the southwest was developing. The temperature distribution at the lowest model level (Fig. 3a) indicates that the low to the southwest developed over a weak warm front. The low to the west moved to the east and the low to the east remained stationary during the day, as dropsondes were collected. By 0000 UTC 30 January, the two lows were oriented from north to south (Fig. 3b) and subsequently combined into one major cyclone on 1 February 1998 (Fig. 3c). The cyclone intensified and moved north-northeastward, with a larger northward component than eastward component of storm motion after 0000 UTC 1 February 1998 (see Fig. 3d, for example). The major cyclone made landfall at 60 N, while the smaller-scale wave associated with the cyclone reached the west coast of the United States. We will focus on the 48-h forecasts initialized at 0000U TC 30 January. We will examine the differences between 3DVAR and 4DVAR analyses and the subsequent forecasts using these analyses, in particular the propagation of the main dropsondes signals with time. b. Description of the 3DVAR and 4DVAR data assimilation experiments The NCEP 3DVAR system (Parrish and Derber 1992) and a recently developed 4DVAR system (Zou et al. 2000) are used to assess the impact of targeted in situ observations (i.e., dropsonde data) over the northern Pacific for the skill of 48- and 72-h forecasts. The observational data used in the experiments include NOR- PEX targeted dropsondes and/or conventional data. The 3DVAR and 4DVAR systems used here do not include satellite radiances whose assimilation are not yet included in the 4DVAR system. The inclusion of satellite radiance data may lead to a different conclusion than without. Minimization of the cost function in both the 3DVAR and 4DVAR is carried out using a perturbation method, which consists of an outer loop (update the analysis variable by an increment) and an inner loop for each outer loop (solve for the increment). Detailed mathematical formulation of the 3DVAR and 4DVAR systems can be found in Zou et al. (2000). Six data assimilation cycles from 0000 UTC 29 January to 0000 UTC 30 January 1998 using the NCEP global spectral model were conducted, with the first 3DVAR/4DVAR minimization being carried out over FIG. 3. The sea level pressures (solid lines) and surface temperatures (dashed lines) at (a) 0000 UTC 29 Jan, (b) 0000 UTC 30 Jan, (c) 0000 UTC 1 Feb, and (d) 0000 UTC 2 Feb 1998.

6 1992 MONTHLY WEATHER REVIEW VOLUME 129 FIG. 4. (a) The rms errors of the CONV3 analysis of wind at 850 hpa at 0000 UTC 30 Jan (b) The difference between the analysis errors of BOTH3 and CONV3 of the wind at 850 hpa at 0000 UTC 30 Jan (c) Same as in (a) except at 500 hpa. (d) Same as in (b) except at 500 hpa. Contour intervals are 2ms 1 for (a) and (c) and 1 m s 1 for (b) and (d), respectively.

7 AUGUST 2001 LIU AND ZOU 1993 DROP3 and DROP4 include only the NORPEX targeted dropsondes, with DROP3 for 3DVAR and DROP4 for 4DVAR. The final analyses at 0000 UTC 30 January 1998 from these six 3DVAR/4DVAR 1-day cycles of experimentation are then used as initial conditions for the subsequent 48-h forecasts initialized at 0000 UTC 30 January Results from these forecasts are examined and compared, along with an additional forecast initialized by the guess field at 0000 UTC 29 (CTRL). Table 1 provides the main features of these six forecast experiments. Other features that are the same for all the experiments are the use of a 6-h data assimilation update cycle, the T62L28 model resolution, and two outer loops with a total of 25 inner loops for each outer loop. FIG. 5. Same as in Fig. 4 except for temperature. Contour intervals are 1 C for (a) and (b) and 0.5 C for (c) and (d), respectively. the time window (t 0 3h, t 0 3h) (t 0 corresponds to 0000 UTC 29 Jan 1998), the second minimization over (t 1 3h, t 1 3h) (t 1 t 0 6h), etc., and the last minimization over (t 5 3h, t 5 3h) (t 5 corresponds to 0000 UTC 30 Jan 1998). The 6-h forecast from the previous analysis time was used as the background field for the subsequent minimization. The experiment CONV3 uses 3DVAR and includes all conventional observational data (radiosondes, marine data, aircraft data, land surface station data, profilers, next-generation Doppler radar wind data, and scatterometer winds). The experiment CONV4 is similar to CONV3 except using the 4DVar system. The experiments BOTH3 and BOTH4 include both the NORPEX targeted dropsondes and conventional observations, with BOTH3 for 3DVAR and BOTH4 for 4DVAR. The experiments 3. Numerical results obtained using 3DVAR/ 4DVAR with and without NORPEX targeted dropsondes As was mentioned before, the average impact of dropsonde and satellite wind data on the 48-h forecasts over NORPEX.FVR for a 6-week period was found to be positive based on the Navy Operational Global Atmospheric Prediction System s optimal interpolation (OI) system (Langland et al. 1999), but dropsondes produced a degradation of the 48-h forecast verified at 0000 UTC 1 February It is not clear whether the forecast degradation is associated with problems in the assimilation techniques or data quality, or something else. The following questions are posed in order to determine the cause of this degradation. 1) Do the targeted dropsondes degrade the 48-h forecast verified at 0000 UTC 1 February 1998 over NOR- PEX.FVR using the NCEP 3DVAR/4DVAR systems described in section 2b? 2) What is the impact of the targeted dropsondes on the 48-h forecast over other regions, instead of NOR- PEX.FVR? 3) Do the 3DVAR and 4DVAR approaches make a difference on the impact of targeted dropsondes? If so, what are the differences? 4) Will the other data sources (such as conventional data) affect the propagation of the dropsondes main signals? If they do, how? In order to gain some insights into these questions, we first discuss the impact of the targeted dropsonde data on the quality of the final analysis after the 1-day assimilation cycles, then the 48-h forecast errors, and finally the temporal evolution of the dropsonde signals. All forecasts are verified using both the conventional and targeted dropsonde data. We realize that an important question concerning the use of conventional observations as verification data is that the Pacific is data sparse. Therefore, the validity of using such data for the estimates of analysis and forecast errors depends on how much data are available for verification. We find that there are very little or no data for specific humidity and

8 1994 MONTHLY WEATHER REVIEW VOLUME 129 FIG. 6. Same as in Fig. 4 except for the background field at 0600 UTC 30 Jan FIG. 7. Same as in Fig. 5 except for the background field at 0600 UTC 30 Jan surface pressure over the Pacific, while more data are available for wind and temperature. Figures 2d e, for example, show the data coverage for the 6-h time period centered at 0000 UTC 30 January Therefore, the verification of analysis and forecast quality using conventional data will be mainly done for wind and temperature fields, keeping in mind that data in some portions of the Pacific Ocean are still sparse. Thus, numerical results provide only a rough estimate on the analysis and forecast quality with and without targeted dropsonde measurements. a. Differences in analyses with and without the targeted dropsondes Figures 4 and 5 show the analysis fit for the experiment CONV3 at 0000 UTC 30 January 1998, 1 ana obs T ana obs RCONV3 (HxCONV3 x )(HxCONV3 x ), (1) N and the difference between the two fits of analyses in BOTH3 and CONV3: TABLE 2. The rms errors of 48-h forecasts verified with conventional observations in NORPEX.FVR of N, W. Expt CONV3 BOTH3 No. of obs T ( C) R R, BOTH3 CONV3 (2) u, (m s 1 ) q (g kg 1 ) p s (mb)

9 AUGUST 2001 LIU AND ZOU 1995 TABLE 3. The rms errors of the 48-h forecasts in FVR (10 70 N, 170 E 130 W), verified with conventional observations. Expt T u, CONV3 COTH3 CONV4 BOTH4 No. of obs where H is an interpolation operator, x obs are observations including both the conventional and the targeted dropsonde data, and N is the total number of observations. We find that the region of maximum analysis error for both wind and temperature is over the northeastern Pacific Ocean (Figs. 4a, 4c, 5a, and 5c). The maximum rms error of wind is 6 m s 1 at 850 hpa and increases to 14 m s 1 at 500 hpa. The maximum rms error of temperature is 3 C at both 850 and 500 hpa. The addition of the NORPEX targeted dropsondes to the conventional data made the largest reduction in the analysis fit over regions where the control analysis with only conventional data contained the largest errors (Figs. 4b, TABLE 4. The rms differences between forecasts and NORPEX targeted dropsonde observations (units: C for temperature and m s 1 for wind). CONV3 BOTH3 CONV4 BOTH4 Expt T (u, ) Forecasts (h) T (u, ) T No. of soundings (u, ) d, 5b, and 5d). Inclusion of the targeted dropsondes produced a closer fit of the analysis to observations over these regions. Figures 4 and 5 show the fit of the analyses to observations. The quality of the analyses, however, can be assessed by examining the fit of the background fields (6-h forecasts) to observations. The results show a better fit of the analysis to observations results in a better fit of the background to observations. Figures 6 and 7, for FIG. 8. The rms errors of the BOTH3 analysis at 0000 UTC 30 Jan 1998 including all vertical levels for (a) wind and (c) temperature, as well as the difference between the analysis errors of BOTH4 and BOTH3 at 0000 UTC 30 Jan 1998 for (b) wind and (d) temperature. Contour intervals are 2 m s 1,1ms 1,2 C, and 0.5 C for (a) (d), respectively.

10 1996 MONTHLY WEATHER REVIEW VOLUME 129 TABLE 5. The rms errors of 48-h forecasts verified with conventional observations in NORPEX.FVR of N, W. Expt T ( C) u, (m s 1 ) q (g kg 1 ) p s (mb) DROP3 DROP example, present the rms errors of the CONV background at 0600 UTC 30 January All the conventional and the targeted dropsonde observations in the 6- h window centered at 0600 UTC 30 January are included in the rms error calculations. These observations are not included in the 1-day cycle of the data assimilation procedure. Except for the wind field at 500 hpa where large negative impact appears to the south of the positive area, the addition of the NORPEX targeted dropsondes results in an overall improvement to the final analysis after a 1-day assimilation cycle over the central and North Pacific Ocean. The performance of the assimilation of the conventional and targeted dropsondes using 4DVAR is shown in Fig. 8. Compared with the results of 3DVAR, the use of 4DVAR produces more improvements in the wind FIG. 10. The differences of the 72-h forecast errors (at 0000 UTC 2 Feb) of (a) wind including all levels and (b) temperature including all levels between BOTH3 and CONV3. Contour intervals are 1 m s 1 and 1 C, respectively. FIG. 9. The 48-h forecast errors at 850 hpa using 3DVAR without NORPEX targeted dropsondes (CONV3) for (a) wind and (c) temperature, as well as the differences between the 48-h forecast errors at 850 hpa with (BOTH3) and without (CONV3) NORPEX targeted dropsondes for (b) wind and (d) temperature. Contour intervals are 2 m s 1,1ms 1,2 C, and 1 C, for (a) (d), respectively.

11 AUGUST 2001 LIU AND ZOU 1997 FIG. 11. Same as in Fig. 9 except using 4DVAR. and temperature analysis over the Pacific Ocean when dropsondes are used. b. Impact of the targeted dropsondes on the 48- and 72-h predictions of the storm Due to differences between the 3DVAR system used here and the OI system used in the work by Langland et al. (1999), we first examine whether the inclusion of the targeted dropsondes in the data assimilation procedure degraded the 48-h forecast verified at 0000 UTC 1 February 1998 over NORPEX.FVR using our version of the NCEP 3DVAR system. The rms errors of the 48- h forecasts in NORPEX.FVR are presented in Table 2. Similar to results obtained in studies by NCEP and NRL, the rms errors for wind with dropsondes (BOTH3) are larger than those without dropsondes (CONV3). However, there is a difference in the magnitude between the rms errors calculated here and those calculated from operational experiments. The larger rms errors shown in Table 2 are at least partly due to the exclusion of all satellite radiance data from our experiments. While the wind and surface pressure forecasts were slightly degraded, we notice that the 48-h forecasts of the temperature and specific humidity over NORPEX.FVR were improved. In order to understand why and how the targeted dropsondes produced forecast degradation, we examine the spatial distributions of the rms differences between the 48-h forecasts and the conventional observations (sometimes we simply call these differences forecast errors) with (BOTH3) and without (CONV3) targeted dropsonde data, and the difference between the two forecast errors (BOTH3 errors CONV3 errors) (Fig. 9). For example, in the wind field at 850 hpa, the control forecast generated a forecast error maximum near the west coast of the United States and near the east Pacific in the midlatitudes (Fig. 9a). These forecast errors are significantly reduced by targeted dropsondes (Fig. 9b). However, the maximum forecast error reduction occurred over the eastern Pacific, west of NORPEX.FVR. For the temperature field, the maximum 48-h forecast errors of the control forecast are located in the middle of the northern Pacific at 850 hpa (Figs. 9c,d). Again, the NORPEX dropsondes produced a forecast improvement over the region of the maximum forecast error in the control forecast. This reduction is as large as 4 C, and, similar to the wind forecast; forecast degradation is seen in the areas surrounding forecast improvement. In summary, the inclusion of the targeted dropsonde measurements available over an area where the storm was developing (BOTH3) generated maximum forecast

12 1998 MONTHLY WEATHER REVIEW VOLUME 129 FIG. 12. The 48-h forecast rms errors at 850 hpa using 3DVAR with NORPEX targeted dropsondes (BOTH3) for (a) wind and (c) temperature, as well as the differences between the 48-h forecast errors at 850 hpa using 4DVAR (BOTH4) and 3DVAR (BOTH3) for (b) wind and (d) temperature. Contour intervals are the same as in Fig. 9. improvement over regions where the control forecast without dropsondes (CONV3) performed worst (i.e., the forecast errors in CONV3 are maximum). The forecast degradation over NORPEX.FVR due to the addition of targeted dropsondes is merely a reflection of the existence of a weak forecast degradation over areas next to the areas of major forecast improvement. The presence of areas of forecast degradation, from targeted dropsonde data producing forecast improvement in other areas through a data assimilation procedure, can also be found in other studies (Toth et al. 1999b; Szunyogh et al. 2000). The 3-day forecast errors with and without dropsondes (Fig. 10) indicate that, first, the NORPEX targeted dropsondes continued to improve the model forecast. Second, the dropsonde signals propagated northward and their impacts to the midlatitude U.S. continent were very minimal. Third, while the main signals of dropsonde data propagated northward, areas of forecast degradation remained and developed upstream and downstream of the main dropsonde signal. c. Dependence of the 48-h forecast impact of the dropsondes on data assimilation approaches The results of 48-h forecast differences using 4DVAR with and without the NORPEX targeted dropsondes (Figs. 11b and 11d) are qualitatively similar to those of 3DVAR (Figs. 9b and 9c); that is, the 48-h forecast improvements (negative areas) occur over the North Pacific Ocean and a weak forecast degradation appears west of the positive impact area. We notice that the forecast errors assimilating only the conventional observations using 4DVAR (Figs. 11a and 11c) are smaller than the corresponding errors using 3D-Var (Figs. 9a FIG. 13. The analysis increments using (left) 3DVAR and (right) 4DVAR after the first minimization at 0000 UTC 29 Jan 1998 using only the targeted dropsondes. (a), (b) zonal wind, (c), (d) meridional wind, (e), (f) temperature, and (g), (h) surface pressure. Contour intervals are2ms 1 for (a) (d), 1 C for (e) and (f), and 2 hpa for (g) and (h).

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15 AUGUST 2001 LIU AND ZOU 2001 and 9c), especially on the U.S. west coast near 50 N for winds 2nd in the North Pacific for temperatures. We now compare the numerical results using 4DVAR (BOTH4) with those using 3DVAR (BOTH3). Both experiments included all the targeted dropsondes and conventional data. The 4DVAR approach produced a better overall improvement to the 48-h forecast than did the 3DVAR approach. Figures 12a and 12b show the 48-h forecast errors of the 850-hPa wind in BOTH3 (Fig. 12a) and the difference in the 48-h forecast errors of the 850-hPa wind between BOTH4 and BOTH3 (Fig. 12b). Significant error reductions are observed due to the use of 4DVAR, when compared with the use of 3DVAR. The error reductions are more than 50% over the northern Pacific region where BOTH3 still had large forecast errors. The large error reduction over the northwestern Pacific Ocean is probably due to the advantages of 4D-Var in assimilating conventional observations. The 48-h forecast error of the temperature at 850 hpa in BOTH3 still had its maximum in the northern Pacific (Fig. 12c), although it was already reduced due to the inclusion of the targeted dropsondes, when compared with CONV3 (see Fig. 9c). The difference of the 48-h forecast errors between BOTH4 and BOTH3 (Fig. 12d) shows an improved 48-h forecast (negative-value area) in the mid Pacific Ocean area, and a 48-h forecast degradation (positive-value area) over the land northeast of the main dropsonde signal. We indicate that the 48-h forecast differences between BOTH4 and BOTH3 are not only due to the different methods of data ingestion of the targeted dropsondes in 4DVAR and 3DVAR, but also due to the different data ingestion of the conventional data in 4DVAR than in 3DVAR. Reasons for the forecast degradation observed upstream and downstream of the main dropsonde signal in both the 3DVAR and 4DVAR experiments are not clear. The rms errors for the 48-h forecast over the northeastern Pacific, such as the area of N, 170 E 130 W, are shown in Table 3. The targeted dropsondes improved the 48-h forecasts of wind and temperature. Improvements of targeted dropsondes to the 48-h forecast are more significant using the 4DVAR approach (BOTH4) than the 3DVAR approach (BOTH3). We indicate that the numbers of observations in the second FVR are less than those over NORPEX.FVR. The conventional data over the ocean consist of all operationally available satellite wind products (low-density IR, visible, and water vapor winds), flight observations, and buoys. Forecast verification with the NORPEX targeted dropsonde observations is presented in the next section. d. Forecast verification with NORPEX targeted dropsonde data The forecast verification by conventional observations over the Pacific Ocean, which was carried out in sections 3b and 3c, is limited by the sparseness of the data. During NORPEX, targeted tropospheric soundings from near flight level (typically ft) to the sea surface were obtained almost every day along the flight tracks with about 200-km horizontal resolution. It will be interesting to compare 1 3-day forecasts with these targeted dropsonde data only. The rms differences between model predictions and NORPEX targeted dropsonde data are presented in Table 4. Assimilations of all the targeted dropsonde data between 0000 UTC 29 January and 0000 UTC 30 January 1998 improved the 1 3-day forecasts verified by the targeted dropsonde data at 0000 UTC 31 January, 0000 UTC 1 February, and 0000 UTC 2 February 1998 using either the 3DVAR or the 4DVAR approach, except for the 24-h forecast of wind using the 4DVAR approach. The geographical locations of the targeted dropsonde data at 31 January, 1 February, and 2 February are shown in Fig. 2c. Except for the 24-h forecast of the wind field, 4DVAR generated more significant improvements than 3DVAR. 4. 3DVAR/4DVAR experiments with only NORPEX targeted dropsondes The NORPEX targeting of dropsonde observations were based on results using SV and ET techniques. Effects of other observations such as conventional and satellite observations were not included in these targeting procedures. In order to separate the impact of conventional observations from the targeted observations, and to gain further insights into the forecast differences that result from using different data assimilation approaches (3DVAR vs 4DVAR), we conducted two dropsonde-only experiments, that is, DROP3 and DROP4. a. Differences in the analysis increments using 3DVAR and 4DVAR Although the same cost function is minimized in the 3DVAR and 4DVAR experiments, the forecast model serves as a strong constraint in the 4DVAR experiment. The analysis increments from the 3DVAR and 4DVAR experiments are different, with those from 4DVAR having a larger area coverage and larger magnitudes of FIG. 14. Differences between the analyses at 850 hpa at 0000 UTC 30 Jan and the 24-h forecast starting from the guess field at 0000 UTC 29 (left) using 3DVAR and (right) 4DVAR, reflecting the impact of the targeted dropsondes on analysis after a 1-day assimilation cycle: (a), (b) zonal wind, (c), (d) meridional wind, (e), (f) temperature, and (g), (h) surface pressure. Contour intervals are the same as in Fig. 13.

16 2002 MONTHLY WEATHER REVIEW VOLUME 129 FIG. 15. The forecast differences of the SLP between (left) DROP3 and CTRL, (middle) BOTH3 and CONV3, and (right) BOTH4 and CONV4 at (a) (c) 0000 UTC 30 Jan, (d) (f) 0000 UTC 31 Jan, and (g) (i) 0000 UTC 1 Feb. The contour interval is (left) 2 hpa and (middle and right) 1 hpa. adjustments, in general. Figure 13, for example, shows the distribution of the analysis increments at 850 hpa after the first minimization at 0000 UTC 29 January 1998 for zonal wind (Figs. 13a,b), meridional wind (Figs. 13c,d), temperature (Figs. 13e,f), and surface pressure (Figs. 13g,h). We find that the 4DVAR analysis increments resemble those of 3DVAR but tend to have more upstream and downstream structures (with a larger spatial scale) around the dropsonde region. After five 6-h assimilation cycles from 0000 UTC 29 January to 0000 UTC 30 January 1998, the differences in the initial model states between 3DVAR and 4DVAR are even larger. Figure 14 shows the differences in the analyzed zonal wind, meridional wind, and temperature at 850 hpa at 0000 UTC 30 January after the 1-day of assimilation cycles and the 24-h forecast starting from the guess field at 0000 UTC 29 January. They reflect the resulting impact of the targeted dropsondes on the model initial condition at 0000 UTC 30 January 1998, which was the initial condition for the 48-h forecast ending at 0000 UTC 1 February We find that the dropsonde signals are stronger in 4DVAR than in

17 AUGUST 2001 LIU AND ZOU DVAR analyses. For instance, the temperature perturbations due to the use of dropsondes, north of Hawaii, are 3 C in 3DVAR and 4 C in 4DVAR. Another obvious difference is the structure of the perturbation fields between 3DVAR and 4DVAR. Besides the main perturbation train oriented north south, the zonal wind perturbation in 4DVAR shows a more complicated west east perturbation. For the meridional wind component, 3DVAR generated a perturbation from northwest to southeast, while in 4DVAR the perturbation is from west to east. These results imply that, although the same 3DVAR background term (J b ) was used in 4DVAR, the model constraint was able to modify the structure of the analysis increments that could otherwise be deduced purely from the background error covariance matrix. b. Propagation of dropsonde signals during the 48-h forecasts We examine the forecast differences between DROP3 and CTRL, BOTH3 and CONV3, and BOTH4 and CONV4. Figure 15, for instance, shows the forecast differences of the surface pressure during the 48-h forecast period. We find that the major signals of the targeted dropsondes propagated southeastward during the 48-h forecast period in the absence of conventional data; that is, the forecast signal from the dropsonde data does travel into the verification region used to construct the NCEP ET target. They, the signals, reached the West Coast at 0000 UTC 1 February 1998 (Fig. 15, left panels). This is different from the results obtained when both conventional data and dropsonde data were used (Fig. 15, middle panels for 3DVAR and right panels for 4DVAR). In those results the dropsonde-induced signals stayed in the northeast Pacific Ocean during the 48-h forecast period. This suggests that a target technique, which accounts for the effects of other existing observations on the analysis, is needed. In order to examine whether the forecast model constraint introduced in 4DVAR during each 6-h assimilation window improves the 48-h forecast over the NORPEX.FVR, we calculated the rms differences between the 48-h forecast in DROP4 (4DVAR with only dropsondes) and the conventional data, as well as the rms differences between the 48-h forecast in DROP3 (3DVAR with only dropsondes) and the conventional data (Table 5). We find that the 48-h forecast starting from the 4DVAR analysis at 0000 UTC 30 January 1998 is, in general, more accurate than that from the 3DVAR analysis. It is encouraging to find that the use of 4DVAR improves the 48-h forecast for all model variables over the NORPEX.FVR when compared with the 3DVAR. Both forecasts are verified by the conventional data and only the targeted dropsonde data are assimilated. 5. Summary and conclusions In this paper we conducted a case study evaluating the impact of NORPEX targeted dropsondes on model prediction, and the abilities of 3DVAR and 4DVAR systems to extract useful information from the targeted dropsonde data. The amount of analysis/forecast improvement due to the use of targeted dropsonde data depends on the data assimilation methods. The propagation of the main dropsonde signals is affected by the inclusion of other data sources, such as conventional data, in the data assimilation procedure. The assimilation of targeted dropsonde data for this NORPEX case reduced the largest downstream 48-h forecast errors, and provided observations that were useful for defining precursor features involved in cyclone development. However, the simultaneous use of the targeted dropsondes and conventional data in data assimilation procedures produced different paths of the main dropsonde signals than were produced when the targeted dropsondes were used alone. The main forecast signal from the dropsonde data did not propagate into the verification region of the NCEP target when conventional observations were included in the assimilation. While the targeted dropsonde data produced improvements in the 2 3 day forecast over an area (away from the NORPEX verification region) where the control forecast with only conventional observations did the worst, forecast degradations are found upstream and downstream of the positive impact areas. This caused a forecast degradation of targeted dropsondes on a 48-h forecast over the NORPEX verification region. For a patch of targeted dropsondes in a relatively small region, 4DVAR tends to produce a more significant forecast improvement over the area where the assimilation without target observations had the largest error. This study suggests that the effects of conventional and/or satellite observations on the magnitude and pattern of initial condition error should be properly accounted for in the targeting procedure. Two key issues for targeting are how best to define the the measure of forecast error chosen in the targeting procedure, and how to account for the effect of incoming conventional and/or satellite observations that were not available at the time of targeting. The exact reason is not clear for the occurrence of forecast degradation over areas upstream and downstream of the main area of forecast improvement generated by the targeted dropsonde observations. Szunyogh et al. (2000) suggested the signals may be related to highly nonlinear data signal development. Further studies are needed on why they exist, how they are generated, and how to eliminate or minimize the negative effect of target observations. Results from this single case study are consistent with the previous studies on the impact of target observations. The single case study allowed us to examine the detailed structures of analysis increment and forecast perturbation. The results obtained in this study are, however, not representative for all NORPEX signal behavior.

18 2004 MONTHLY WEATHER REVIEW VOLUME 129 More case studies are needed before we can draw any conclusions in a statistical sense. Acknowledgments. This research is supported by the National Science Foundation under Project ATM , as well as the Office of Naval Research under Project N The authors would like to thank Drs. S. Lord and Z. Toth for the case selection. Finally, we are very grateful to Dr. R. Langland for helping to improve the manuscript. REFERENCES Bishop, C. H., and Z. Toth, 1999: Ensemble transformation and adaptive observations. J. Atmos. Sci., 56, Daley, R., 1991: Atmospheric Data Analysis. Cambridge University Press, 457 pp. Gelaro, R., R. Buizza, T. N. Palmer, and E. Klinker, 1998: Sensitivity analysis of forecast errors and the construction of optimal perturbations using singular vectors. J. Atmos. Sci., 55, Langland, R. H., and Coauthors, 1999: The North Pacific Experiment (NORPEX-98): Targeted observations for improved North American weather forecasts. Bull. Amer. Meteor. Soc., 80, Parrish, D. F., and J. Derber, 1992: The National Meteorological Center s spectral and statistical-interpolation analysis system. Mon. Wea. Rev., 120, Szunyogh, I., Z. Toth, K. A. Emanuel, C. H. Bishop, C. Snyder, R. E. Morss, J. Woolen, and T. Marchok, 1999a: Ensemble-based targeting experiments during FASTEX: The effect of dropsonde data from the Lear jet. Quart. J. Roy. Meteor. Soc., 125, ,, S. Majumdar, R. Morss, C. Bishop, and S. Lord, 1999b: Ensemble-based targeted observations during NORPEX. Preprints, Third Symp. on Integrated Observing Systems, Dallas, TX, Amer. Meteor. Soc., ,, and R. E. Morss, 2000: The effect of targeted dropsonde observations during the 1999 Winter Storm Reconnaissance Program. Mon. Wea. Rev., 128, Toth, Z., and Coauthors, 1999a: Targeted observations at NCEP: Toward an operational implementation. Preprints, Fourth Symp. on Integrated Observing Systems, Long Beach, CA, Amer. Meteor. Soc., , I. Szunyogh, S. Majumdar, R. Morss, B. Etherton, C. Bishop, and S. Lord, 1999b: The 1999 Winter Storm Reconnaissance program. Preprints, 13th Conf. on Numerical Weather Prediction, Denver, CO, Amer. Meteor. Soc., Zou, X., H. Liu, J. Derber, J. G. Sela, R. Treadon, I. M. Navon, and B. Wang, 2000: Four-dimensional variational data assimilation with a diabatic version of the NCEP global spectral model: System development and preliminary results. Quart. J. Roy. Meteor. Soc., in press.