Impact of Infrared, Microwave, and Radio Occultation Satellite Observations on Operational Numerical Weather Prediction

4164 MONTHLY WEATHER REVIEW VOLUME 142

L. CUCURULL Global Systems Division, NOAA/OAR/Earth System Research Laboratory, and Cooperative Institute for Research in Environmental Sciences, Boulder, Colorado

R. A. ANTHES University Corporation for Atmospheric Research, Boulder, Colorado

(Manuscript received 1 April 2014, in ﬁnal form 4 August 2014)

ABSTRACT

A comparison of the impact of infrared (IR), microwave (MW), and radio occultation (RO) observations on NCEP’s operational global forecast model over the month of March 2013 is presented. Analyses and forecasts with only IR, MW, and RO observations are compared with analyses and forecasts with no satellite data and with each other. Overall, the patterns of the impact of the different satellite systems are similar, with the MW observations producing the largest impact on the analyses and RO producing the smallest. Without RO observations, satellite radiances are over– or under–bias corrected and RO acts as an anchor observation, reducing the forecast biases globally. Positive correlation coefficients of temperature impacts are generally found between the different satellite observation analyses, indicating that the three satellite systems are affecting the global temperatures in a similar way. However, the correlation in the lower troposphere among all three systems is surprisingly small. Correlations for the moisture field tend to be small in the lower troposphere between the different satellite analyses. The impact of the satellite observations on the 500-hPa geopotential height forecasts is much different in the Northern and Southern Hemispheres. In the Northern Hemisphere, all the satellite observations together make a small positive impact compared to the base (no satellite) forecasts. The IR and MW, but not the RO, make a small positive impact when assimilated alone. The situation is considerably different in the Southern Hemisphere, where all the satellite observations together make a much larger positive impact, and all three observation types (IR, MW, and RO) make similar and significant impacts.

1. Introduction to rise or set relative to Earth. The arrival time of the received radio signal is retarded because of the refractive Radio occultation (RO) observations have recently bending and slowing of the signal as it traverses the at- (since late 2006) been shown to have a positive impact mosphere. By measuring the change in phase measure- on global numerical weather prediction, complementing ments during the occultation event, proﬁles of bending infrared (IR) and microwave (MW) observations from angle, atmospheric refractivity, pressure, temperature, satellites (Cardinali 2009; Anthes 2011; Bonavita 2014; and water vapor can be retrieved as a function of altitude Healy 2008, 2013; Poli et al. 2009; Cucurull 2010; Radnóti (Hajj et al. 1994, 2002; Rocken et al. 1997; Kursinski et al. et al. 2010; Rennie 2010; Aparicio and Deblonde 2008; 2000; Kuo et al. 2004). GPS operates at very long wave- Bauer et al. 2014). A radio occultation occurs when a lengths compared to other remote sensing instruments, receiver on a low-Earth-orbiting (LEO) satellite tracks and its frequencies are below any absorption lines. In a global positioning system (GPS) satellite that is observed addition, particle scattering is negligible. Thus, the RO technology does not suffer from scattering and absorption effects as other instruments operating at shorter Corresponding author address: Lidia Cucurull, Global Systems Division, Ofﬁce of the Director, NOAA/Earth System Research wavelengths do (Melbourne et al. 1994). Laboratory (ESRL), 325 Broadway, R/GSD, Boulder, CO 80305. Observations made by radiometry of absorption and E-mail: [email protected] emission spectra from natural sources usually are

DOI: 10.1175/MWR-D-14-00101.1

Ó 2014 American Meteorological Society Unauthenticated | Downloaded 09/23/21 07:17 PM UTC NOVEMBER 2014 C U C U R U L L A N D A N T H E S 4165 obtained from amplitude measurements that are more frequently run at the operational numerical weather vulnerable to long-term stability errors in their calibra- prediction centers (e.g., Radnóti et al. 2010; McNally tion systems. As a result, most atmospheric remote et al. 2014). Comparison between OSEs and FSO results sensing instruments require calibration on orbit. In has been investigated by Gelaro and Zhu (2009). Todling contrast, the RO technology uses very precise phase (2013) suggests a new approach to evaluate the impact of measurements of artificial signals. Because the RO current observations in weather forecasting based on the technique is free of instrumental biases, it can serve as ‘‘observation space’’ approach rather than the ‘‘state a calibration system for other instruments. For example, space’’ approach used in the tangent linear methods. Zou et al. (2014) used precise and accurate RO obser- However, detailed studies of the differences in how the vations to calibrate upper temperature channels from three satellite systems (MW, IR, and RO) affect the the Advanced Technology Microwave Sounder analyses and subsequent forecasts have not been done. (ATMS) on board the Suomi National Polar-Orbiting The goal of this paper is to investigate the differences Partnership (Suomi-NPP) satellite. and similarities between the impacts on global temper- Nadir-viewing instruments provide information asso- ature and moisture analyses and forecasts associated ciated with a weighted average over atmospheric layers with the separate assimilation of RO, MW, and IR ob- of different thicknesses, which limits the vertical reso- servations in the National Centers for Environmental lution of these observations. Increasing the number of Prediction (NCEP) operational global data assimilation channels improves the vertical resolution, but radiance system. Where these analyses and forecasts differ sig- emitted over a scale height ultimately limits the resolu- nificantly from each other or from the model analysis tion that can be achieved. In contrast, RO provides high- without the satellite observations can indicate possible vertical-resolution profiles. However, the horizontal issues with the satellite systems and/or the model. footprint associated with the nadir geometry is smaller A description of the different experiments is pre- than the one associated with a limb-viewing technology sented in section 2. The methodology used to evaluate (Kursinski et al. 1997), which enables the nadir-viewing the results is presented in section 3. Section 4 analyzes radiance sounders to have higher horizontal resolution the results of the experiments, and the main conclusions than RO. Thus, the two systems are highly comple- are summarized in section 5. mentary [e.g., Collard and Healy (2003) and as summarized by WMO (2012)]. 2. Experiments The assimilation of satellite radiances in operational weather forecasting benefits from the assimilation of We conducted six experimental sets of 39 forecasts unbiased observations that reduce the drift of a weather over the period 21 February–31 March 2013. The control model toward its own climatology (Derber and Wu experiment CTL assimilated all the observations used in 1998; Dee 2004, 2005; Dee and Uppala 2008). The role of the operational suite of the NCEP’s global data assimi- RO observations as ‘‘anchor observations’’ in NWP lation system, including RO and satellite radiance ob- models and the resulting improvements in the bias cor- servations. A second experiment, BASE, removed all rection of the satellite radiances has been shown in satellite sounding systems (IR, MW, and RO) while a number of studies (Healy et al. 2005; Healy and Thépaut retaining all other observations. Experiments 3–5 (des- 2006; Healy 2008; Poli et al. 2010; Bauer et al. 2014; ignated IR, MW, and RO) selectively added back to the Cucurull at al. 2014; Bonavita 2014). BASE the IR, MW, and RO observations. A summary Active RO limb soundings and passive MW and IR of the five experiments is provided in Table 1. All the nadir-viewing instrument systems on satellites produce experimental forecasts begin at 0000 UTC and run for information on the three-dimensional temperature and 8 days (192 h) from 21 February to 31 March 2013. The water vapor structure in the atmosphere and are to- first 7 days are used for model spinup, and the compar- gether the most effective space-based observational sys- isons cover the period 28 February–31 March 2013. The tems in reducing forecast errors (Anthes 2011; Cardinali horizontal resolution of the operational NCEP’s global and Prates 2011; Cardinali and Healy 2014). Several data assimilation at the time of this study is T574 studies have shown the contribution of the different (;27 km) with 64 levels in the vertical. The model top is systems to short-range (24 h) forecast accuracy using the located at 0.266 hPa (;60 km). Satellite radiances are Forecast Sensitivity to Observations (FSO) method thinned to 145 km and bias corrected at NCEP using (Baker and Daley 2000; Langland and Baker 2004; Errico a variational bias correction approach in its hybrid three- 2007; Gelaro et al. 2007; WMO 2012). The FSO tech- dimensional variational data assimilation (3DVAR) sys- nique uses the adjoint of a data assimilation system. In tem (Derber and Wu 1998; Dee 2004; Dee and Uppala addition, observing system experiments (OSEs) are 2008). The variational bias correction is active in all the

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TABLE 1. Summary of the different experiments conducted in this study.

Expt name Characteristics CTL Control experiment. Assimilates all the observations used operationally in the NCEP’s global data assimilation system. The satellite radiances include (IR) High Resolution Infrared Radiation Sounder 4 (HIRS/4) on Meteorological Operational A (MetOp-A) and National Oceanic and Atmospheric Administration-19 (NOAA-19), Infrared Atmospheric Sounding Interferometer (IASI) on MetOp-A, Atmospheric Infrared Sounder (AIRS) on Aqua, and the sounders from the GOES-15 geostationary satellite (for IR) and Advanced Microwave Sounding Unit A (AMSU-A) on NOAA-15, NOAA-18, NOAA-19, MetOp-A, and Aqua; Microwave Humidity Sounder (MHS) on NOAA-18, NOAA-19, and MetOp-A; and ATMS on Suomi-NPP (for MW). The assimilation of RO includes data from Constellation Observing System for Meteorology, Ionosphere and Climate (COSMIC), Global Navigation Satellite System Receiver for Atmospheric Sounding (GRAS) on MetOp-A, Gravity Recovery and Climate Experiment–A (GRACE-A), Terra synthetic aperture radar operating in X band (TerraSAR-X), and Communications/Navigation Outage Forecasting System (C/NOFS). BASE Baseline experiment. As in CTL, but removing all the RO and satellite radiances (both the IR and MW). IR As in BASE, but with the infrared satellite radiances. MW As in BASE, but with the microwave satellite radiances. RO As in BASE, but with the radio occultation data. experiments that assimilate satellite radiances. The analyzed. The T or q analyses are interpolated to the background error statistics are computed online through time and location of all available, high-quality (as esti- an ensemble Kalman filter for all the experiments. Ex- mated by the NCEP quality-control system) radio- periment RO assimilates ;400 000 bending angles daily. sondes globally. The global and temporal (32 days) RO profiles are assimilated up to 50 km in impact height means of the differences are calculated for each vertical [see Cucurull et al. (2013) for details on the forward op- level. These profiles show where, on average, the dif- erator used at NCEP]. On average, there are ;8 million ferent sounding systems improve or degrade the BASE IR and ;3.5 million MW radiance observations assimi- analysis compared to radiosondes. They are biased to- lated daily. (Statistics for satellite radiances and RO data ward land areas of the Northern Hemisphere where used operationally at NCEP can be found at www.emc. most of the radiosonde observations are. ncep.noaa.gov/gmb/gdas, and details on the use of satellite Second, horizontal maps at different levels of the radiances in the CTL experiment that include the sensors mean differences between the IR, MW, and RO tem- and channels being used can be found at www.emc.ncep. perature and specific humidity analyses compared to the noaa.gov/gmb/wx20cl/radiances/OSSE/OSE/prctl/.) BASE analysis are analyzed. These differences are We chose the strategy of adding the satellite systems called the analysis impacts of IR, MW, and RO obser- of observations to the BASE experiment that does not vations. They show horizontal patterns of a month’s contain the three systems one at a time, rather than an average of the difference between analyses using the alternative strategy of omitting each system one at different satellite observations and the BASE analysis. a time from a base that contained all three systems These bias differences may be caused by either biases (data denial experiments). Both strategies provide in the sounding systems or the BASE, or a combination useful information. The strategy followed here, which of both. was also followed by Bonavita (2014), allows for an Third, we analyze horizontal maps at different levels enhanced and clearer impact of each individual system of the root-mean-square (RMS) differences between the because the complex interactions with the other sys- IR, MW, and RO analyses compared to the BASE tems do not occur. analysis. These charts are a measure of the temporal and spatial variability of the impacts on the analysis from the 3. Analysis of the forecasts three sounding systems compared to the BASE analysis. Where the RMS values are small, the sounding system We make five types of analyses of the differences in produces little impact on the BASE analysis during the temperature (T) and water vapor (q, specific humidity) 32-day period. Where the RMS values are large, the among the five experiments (see appendix for mathe- analyses from the BASE and the sounding systems show matical definitions). greater differences from day to day. First, vertical profiles of the fit to radiosondes of the Fourth, we analyze vertical profiles of global and CTL, BASE, IR, MW, and RO analyses of T and q are temporal averages of

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1) mean differences of temperature and specific humid- minimum at the tropopause over the entire Earth would ity for IR–BASE, MW–BASE, RO–BASE, IR–MW, be a relatively thick layer of warm bias. All the experi- IR–RO, and MW–RO; ments show this warm bias, indicating that it is not 2) RMS differences of temperature and specific humid- caused by satellite data. ity for IR–MW, IR–RO, and MW–RO; and The MW and IR experiments show the greatest warm 3) global mean correlations of the mean monthly anal- bias at 200 mb. This might be due to the fact that the lack ysis impacts (differences from BASE)asafunctionof of unbiased observations in these two experiments en- vertical level among IR–MW, IR–RO,andMW–RO. hances the effects of the warm-biased aircraft data around this level. The RO experiment shows a smaller These profiles show quantitatively where the three peak bias at 200 mb, but the bias is still 0.25 K. sounding systems agree most closely on their impact on From 300 to 800 mb the biases are all under 0.1 K, but the BASE analyses and where they differ. the larger warm bias in the MW stands out from the Finally, standard forecast skill verification, including other experiments. It is noticeable that MW produces anomaly correlation (AC) score and bias, is evaluated the largest cold bias in the stratosphere and the warmest against a consensus analysis from NCEP, the European bias in the troposphere. Centre for Medium-Range Weather Forecasts (ECMWF), At 925 mb, all analyses show a noticeable cold bias of and Met Office analyses. about 20.1 K, possibly related to model errors in resolving the location and structure of the atmospheric 4. Results boundary layer (ABL). Different topographies and surface effects make it difficult to compare model a. Fit of analyses to radiosondes analyses and observations at these lower heights. We first look at the fit of the five analyses to radiosondes. The fit of the q analyses to radiosondes (Fig. 2b) shows Figure 1 shows the mean over all analyses at t 5 0 tro- a drier stratosphere (above 200 mb) and a moister tropopause height and pressure level for the CTL experi- posphere, with moisture impact maxima at 700 and ment. The mean tropopause height varies from more 925 mb, with a relative minimum at 850 and 1000 mb. All than 17 km [less than 90 mb (1 mb 5 1 hPa)] in the these analyses show the same shape, indicating that tropics to less than 8 km (more than 240 mb) in the polar some other factor in the model analyses is causing this regions. pattern (there is no reason to think that the IR, MW, and Figure 2 shows the fit of T and q to the radiosondes of RO observation errors are correlated in the vertical). the five analyses (analysis observations). All the analy- The cold and moist bias at 925 mb compared to radio- ses except RO are cold compared to the radiosondes in sondes in this study is consistent with what is seen in the the stratosphere (above 100 mb). The cold bias in the NCEP’s forecast system verification (S. Saha 2014, NCEP stratospheric analyses and forecasts is well personal communication). known (Yang 2013). The ability of the unbiased RO b. Global distribution of mean differences between observations to reduce or eliminate biases in the satellite analysis and BASE stratosphere is also well known (Healy et al. 2005; Healy and Thépaut 2006; Healy 2008; Poli et al. 2010; Cucurull Figure 3 shows the global distribution of the monthly at al. 2014). It is interesting that the MW and IR obser- mean differences of temperature at 850 mb from the IR, vations increase the cold bias, while the BASE (no sat- MW, and RO analyses and the BASE analysis. Most of ellite observations) actually has a smaller bias. RO the impacts of the three satellite systems are within observations act as ‘‘anchor’’ measurements, reducing 60.5 K in the monthly mean. As expected, the greatest a drift of the model to its own climatology. As a conse- differences are in the Southern Hemisphere, where quence, the bias correction of radiance observations is there are fewer radiosondes and other nonsatellite improved, which results in better analyses. observations. The MW analysis stands out in its much The warm bias in the upper troposphere that peaks warmer Arctic region;1 its strong region of warming around 200 mb in all experiments is very likely due to the over northern Africa; and its warmer analysis over the assimilation of aircraft observations, which have a warm bias of up to 0.5 K (Cardinali et al. 2003; Ballish and Kumar 2008; B. Ballish 2012, meeting presentation). In 1 addition, the model does not represent the tropopause The unphysical extended warmer area in MW at the very high latitudes, which are probably related to difficulties in estimating the well, smoothing the cold tropopause. The tropopause surface emissivity over sea ice, has been recently improved at level varies considerably over the globe (Fig. 1), and so NCEP with the use of an enhanced satellite bias correction (Zhu the net bias introduced by smoothing the temperature et al. 2013) and some quality-control fixes.

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FIG. 1. Temporal tropopause mean (top) height and (bottom) pressure of all analyses over all 32 times in the CTL experiment.

North Atlantic, the Southern Ocean, and western the similar cooling pattern found in the IR, MW,and Antarctica. Both the IR and RO analyses show cooler RO experiments. patterns over the Southern Ocean. All three analyses Figure 4 shows the global distribution of the mean show a cooler region off the western coast of South differences between the specific humidity at 850 mb America, where the marine ABL is especially well from the IR, MW, and RO analyses and the BASE. The defined. The uncertainty in the model of the exact IR and MW water vapor impacts (Figs. 4a,b) show location of this layer in this poorly observed region similar patterns: wetter over Africa, off the west coasts could lead to sharp maxima in the background error of North and South America, and over the tropical At- profiles. This would result in similar large analysis lantic, and drier over Australia and off the eastern coast increments for all three observing systems, justifying of South America. The RO impacts are significantly less,

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21 FIG. 2. Vertical profiles of (a) temperature (K) and (b) specific humidity (g kg ) fit to radiosondes as a function of pressure level of CTL (purple), BASE (black), IR (blue), MW (green), and RO (red). Note the different scale in the y axes. although there are similarities in patterns to the IR and At 500 mb (figures not shown) the dominant feature in MW, particularly the IR. the temperature impacts for all three satellite systems is Figure 5 shows the global distribution of the mean a strong cooling in the ocean surrounding Antarctica, differences between the temperature at 700 mb from between 508 and 708S. The pattern is similar to that the IR, MW,andRO analyses and the BASE analysis. shown in Fig. 5, but the magnitude of the cooling is All three satellite sounding systems show warm im- larger, exceeding 22.5 K in MW. The magnitude of the pacts over eastern Antarctica and cooling over the cooling is least in RO, with a maximum of about 21.5 K. ocean surrounding Antarctica, 508–708S and centered The main feature in the specific humidity impacts at at 608S. The cooling impacts are greatest for MW (over 500 mb for all three satellite systems is a large region of 22 K) and least for RO (;21K). MW shows large drying from the equator southward to 308S in the Indian warm impacts over the Arctic, while IR shows weak Ocean, from 108Nto308S in the Pacific Ocean, and from warming and RO shows little change. In general, RO the equator to 208S in the Atlantic Ocean. Maximum 2 shows the least change compared to the BASE,prob- values of drying are about 1.5 g kg 1 in MW and IR and 2 ably because of the relatively small number of RO about 0.5–1.0 g kg 1 in RO. The patterns are similar. observations. Outside these regions moisture impact is small, less than 2 Figure 6 shows the global distribution of the mean 60.5 g kg 1. differences between the specific humidity at 700 mb To illustrate the impacts at high levels, we show the from the IR, MW, and RO analyses and the BASE temperature impacts at 100 mb in Fig. 7. The dominant analysis. All three satellite systems slightly moisten the temperature impact at 100 mb is a warming of over 1 K air at 700 mb over Antarctica. IR, MW, and to some in the subtropical Pacific. The magnitude and shape is extent RO show an elongated band of moistening ex- similar in all three satellite systems. There are also weak tending east-southeastward from Africa, across the In- cooling impacts noted over east Antarctica (308–1508E) dian Ocean and Indonesia and into the South Pacific. in all three systems and weak cooling off the Antarctica The three sounding systems also provide a drying impact coast between 1208 and 608W in the IR and RO experi- over the equatorial Pacific and off the west coast of ments, but not the MW. Australia. Again, the RO impacts are generally smaller The preceding discussion has shown where biases than the IR or MW. exist over the month of March 2013 between the

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FIG. 3. Global distribution of the monthly mean temperature differences (K) at 850 mb from the (a) IR, (b) MW, and (c) RO analyses and the BASE analysis. analyses from the three satellite observing systems and c. Global distribution of RMS differences between the BASE analyses. Only a few regions show bias dif- satellite analysis and BASE ferences greater than 60.5 K. Of course, the satellite systems are expected to have much greater impacts from Figure 8 shows the RMS differences between the IR, day to day, especially in the Southern Hemisphere. This MW,andRO analyses compared to the BASE analysis is indeed what we ﬁnd, and we illustrate the results at for temperature at 500 mb. The striking features of two levels, 500 and 850 mb. Fig. 8 are the small RMS impacts over the continents

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21 FIG. 4. Global distribution of the monthly mean speciﬁc humidity differences (g kg ) at 850 mb from the (a) IR, (b) MW, and (c) RO analyses and the BASE analysis.

(except Africa) where radiosonde and other data are associated with RO are the smallest (maximum of plentiful and the band of large RMS impacts extending 2.5 K). Smaller regions of impacts between 1.0 and around the globe between 408 and 708S, where the day- 1.5 K exist over the Indian Ocean from the equator to to-day variability of weather associated with this 408S and over the North Paciﬁc in all three satellite strong baroclinic zone and storm tracks together with systems. lack of other data make the satellite data especially Figure 9 shows the RMS differences between the IR, impactful. RMS differences associated with MW are MW, and RO speciﬁc humidity analyses compared to the the greatest, exceeding 3 K, while the RMS differences BASE analysis at 500 mb. In contrast to the temperature

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FIG. 5. Global distribution of the monthly mean temperature differences (K) at 700 mb from the (a) IR, (b) MW, and (c) RO analyses and the BASE analysis.

impacts shown in Fig. 8, the speciﬁc humidity impacts Oceans. As with temperature impacts, the magnitude of from the three satellite systems are greatest over the the RO water vapor impacts are slightly less those for tropics and subtropics (308N–308S), with typical values the IR or MW impacts. 2 2 1.0–1.5 g kg 1, but reaching as high as 2.0 g kg 1 in the Figure 10 shows the RMS differences between the IR, MW analysis over the equatorial Paciﬁc and Atlantic MW, and RO temperature analyses compared to the

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21 FIG. 6. Global distribution of the monthly mean speciﬁc humidity differences (g kg ) at 700 mb from the (a) IR, (b) MW, and (c) RO analyses and the BASE analysis.

BASE analysis at 850 mb. At 850 mb, the maximum present challenges for the model physics and the satel- impacts are generally in the Southern Hemisphere, with lite observations. All three analyses show large RMS maxima over Antarctica, the Southern Ocean, and the differences over the tropical North Pacific. The MW South Pacific. A region of exceptionally large impacts is analysis shows a strong maximum over the Arctic, which off the coast of South America, where the RMS differ- occurs to a lesser degree in the IR analysis and is only ences exceed 3 K. This is the region of a well-defined barely discernable in RO. marine boundary layer where there are extremely sharp Figure 11 shows the RMS differences between the IR, vertical gradients in temperature and water vapor as MW, and RO specific humidity analyses compared to the well as a persistent marine stratocumulus deck, which BASE analysis at 850 mb. The water vapor impacts

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FIG. 7. Global distribution of the monthly mean temperature differences (K) at 100 mb from the (a) IR, (b) MW, and (c) RO analyses and the BASE analysis.

2 (Fig. 11) show similar patterns in IR, MW, and RO. High itself. Strongest maxima of over 2 g kg 1 exist over values extend across the globe north and south of the equatorial Africa, the Indian Ocean, tropical North equator to about 408N and 408S, respectively. There is Paciﬁc, and Caribbean and off both the east and west a weak relative minimum in impacts over the equator coasts of South America.

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FIG. 8. RMS differences between the (a) IR, (b) MW, and (c) RO temperature analyses (K) compared to the BASE analysis at 500 mb.

The results of these comparisons show that, in general, for the relatively small RO impacts is almost certainly the patterns of the impacts of IR, MW, and RO are due to the fact that there are far fewer RO observations similar. The MW impacts are usually largest and the RO than either IR or MW satellite radiances. However, it is impacts are the smallest, with IR in between. The reason encouraging that these comparisons show impacts from

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21 FIG. 9. RMS differences between the (a) IR, (b) MW, and (c) RO specific humidity analyses (g kg ) compared to the BASE analysis at 500 mb. all three satellite systems that generally agree in pattern satellite systems, IR, MW, and RO. To see how the global and in magnitude. means of these impacts vary in the vertical, as well as how the global mean impacts among the three systems vary d. Vertical profiles of global and temporal averages among themselves, we plot the vertical profiles of of mean and RMS differences and correlations 1) global mean analysis minus BASE temperatures; between the different analyses 2) global mean IR–MW, IR–RO,andMW–RO tempera- The preceding discussion has illustrated differences ture impacts; and 3) correlation coefficients among IR– between the analysis impacts and the BASE for the three MW, IR–RO,andMW–RO temperature impacts in Fig. 12.

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FIG. 10. RMS differences between the (a) IR, (b) MW, and (c) RO temperature analyses (K) compared to the BASE analysis at 850 mb.

The vertical proﬁles of the global mean impacts of IR proﬁle of MW impacts is quite different from the IR and and RO are similar, showing a cooling of 20.1 to 20.2 K RO impacts, showing a warming of up to 0.2 K from the from the surface to about 300 mb. From 300 to 100 mb surface to 800 mb, cooling up to 20.2 K from 700 to they show a warming of up to 0.2 K. Above 100 mb, 400 mb, warming between 300 and 200 mb, and then where the cold bias exists in the NCEP analysis, IR cooling above 200 mb. The more pronounced vertical shows a slight cooling while RO shows a warming, which structure correction from MW in the lower troposphere is offsetting the cold bias, by up to 0.4 K. The vertical seems to be associated with the larger analysis

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21 FIG. 11. RMS differences between the (a) IR, (b) MW, and (c) RO specific humidity analyses (g kg ) compared to the BASE analysis at 850 mb. differences in the high latitudes, particular the polar differences shown in Fig. 12a. In terms of RMS differ- regions (Figs. 3b, 5b). ences (Fig. 12c), the largest variability exists between Another way of looking at the difference in impacts is the MW and RO analyses. In all three cases, the largest to compare the vertical profiles of impacts from the RMS differences are found in a layer between 800 and three satellite systems with each other (Fig. 12b). This 900 mb. chart shows the relative similarity of IR and RO except Finally, we consider the correlation of the global mean above 100 mb, where IR and RO impacts in the strato- impacts among the three satellite systems in Fig. 12d. sphere are of opposite sign. The oscillating profiles Globally averaged correlation coefficients of pairs of of IR–MW and MW–RO in Fig. 12b highlight the time-averaged analysis impacts are computed. Here we

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FIG. 12. Vertical profiles of (a) global monthly mean IR, MW, and RO analyses minus BASE temperatures, (b) global monthly mean IR–MW, IR–RO, and MW–RO temperature impacts, (c) RMS differences for IR–MW, IR–RO, and MW–RO temperature impacts, and (d) correlation coefficients among monthly mean IR–MW, IR–RO, and MW–RO temperature impacts. see positive correlations exceeding 0.6 from 700 to negative near 200 mb. That IR observations cannot 200 mb between pairs of all three systems (except for penetrate through clouds, that both IR and MW data are a layer between 350 and 300 mb for MW–RO). These affected by errors in surface emissivity and skin tem- relatively high correlations indicate that the three sat- perature, and that RO data are limited in number and ellite systems are affecting the global temperatures in may experience superrefraction in the tropics may ex- generally the same way. However, the correlation in the plain the low correlations between the three systems in lower troposphere (surface to 800 mb) among all three the lower troposphere. systems is surprisingly low (less than 0.5), and in the case Figure 13 shows the corresponding vertical profiles of of MW and RO, the correlation is even negative below global mean and impact differences and their correla- 900 mb. The correlation between MW and IR is also tions for specific humidity. Overall, all three satellite

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FIG. 13. Vertical profiles of (a) global monthly mean IR, MW, and RO analyses minus BASE specific humidity, (b) global monthly mean IR–MW, IR–RO, and MW–RO specific humidity impacts, (c) RMS differences for IR–MW, IR–RO, and MW–RO specific humidity impacts, and (d) correlation coefficients among monthly mean IR–MW, IR–RO, and MW–RO specific humidity impacts. systems produce drier analyses than the BASE at ;800 mb, which is generally above the ABL height. (Fig. 13a). However, MW tends to moisten the analyses This seems to indicate that the ABL structure is domi- below 750 mb, and to a small extent, so does IR below nated by the assimilation of conventional observations 850 mb. Both IR and MW analyses are drier than RO and/or model physics. Overall, the differences in RMS analyses between 650 and 350 mb. This is also reflected between the experiments are small. The RMS differ- in Fig. 13b, where the differences between the mean ences between IR and MW are the smallest, which is analyses impacts for pairs of satellite systems are shown. expected since we have seen that RO analyses do not Below 700 mb, MW is wetter than RO and IR, and IR is impact the moisture field as much as the IR and MW wetter than RO below 800 mb. The largest RMS differ- analyses (Figs. 4, 6). Because the magnitude of the im- ences (Fig. 13c) between the analyses impacts are found pacts of RO is small compared to the magnitude of the

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MW and IR impacts, the RMS differences between the RO and MW and IR impacts will be larger than the RMS differences between MW and IR, even if the impacts are highly correlated. Correlations between the different monthly mean impacts are shown in Fig. 13d. IR and MW are highly correlated above ;700 mb. However, their correlation is surprisingly low (,0.6) in the lower troposphere. The lower vertical resolution associated with the IR and MW observations might not be good enough to resolve the boundary layer structures and to correct for the large model errors present in this region. Also, surface emissivity and skin temperature errors and the presence of clouds affect the IR and MW differently in the lower troposphere. The vertical profiles of the correlation coefficients shown in Figs. 12 and 13 are profiles of the correlations among the monthly mean impact fields at each vertical level; they help quantify the similarities among the patterns shown in the horizontal maps of monthly mean impacts shown in Figs. 3–7. Another way of showing how closely the three different satellite systems impact the BASE analysis is to compute the monthly average of the daily correlation coefficients of pairs of the impacts. Vertical profiles of these correlation coefficients are shown in Fig. 14. The vertical profiles of the mean daily temperature impact correlations (Fig. 14a) are similar in shape to the corresponding profiles of the monthly mean impacts (Fig. 12d), but they are somewhat smoother and do not show any levels of negative correlations. The correlations between IR and MW are larger than those between IR and RO and MW and RO, which may be due to the greater independence of the active limb-scanning RO observations compared to the passive, nadir- viewing radiance observations. The correlations between all the pairs are least below 800 and above 400 mb. The vertical profiles of the correlations of the mean daily specific humidity impacts (Fig. 14b) are also smoother than the corresponding profiles of the monthly mean correlations (Fig. 13d) and show higher values in the lower troposphere. As with temperature, the specific FIG. 14. Vertical profiles of correlation coefficients among humidity correlations are higher for IR and MW than for monthly means of daily IR–MW, IR–RO, and MW–RO (a) tem- IR–RO or MW–RO. perature and (b) specific humidity impacts. e. AC score and biases of extended forecasts the greatest effect, as expected, and these impacts are The preceding results have considered the differences statistically significant at all forecast times. The next and similarities of the five analyses. This section com- largest effect is MW, followed by IR, and both of these pares the accuracy of the forecasts made from these experiments show differences that are statistically sig- analyses. nificant out to 120 h (5 days). RO shows no significant Figure 15 shows the 500-mb geopotential heights AC differences compared to BASE. In the SH the situation score for the five experiments out to 192 h (8 days) for is quite different. The CTL again shows the greatest the Northern (NH) and Southern Hemispheres (SH). impact, but MW, IR, and RO all show significant impacts The satellite observations have a relatively small effect and all three are similar in magnitude. The impact of in the NH. The CTL (all satellite observations) shows satellite observations in the SH is much more significant

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FIG. 15. Anomaly correlation score for the 500-mb geopotential heights for BASE (black), IR (red), MW (green), RO (blue), and CTL (orange) for (a) NH and (b) SH. Bottom panels show differences with respect to BASE, with positive being an improvement. Bars show limits of statistical significance at the 95% confidence level; values above bars are statistically significant. Only the CTL experiment differences are statistically significant after day 5 in the NH. In the SH, all differences are statistically significant for all forecast lead times. than in the NH because of the limited number of con- biases are smaller and positive (warmer). This result is ventional observations in the SH (Kelly and Pailleux consistent with Fig. 2a. 1988). The zigzag structure seen in the biases at 50 and 850 mb Figure 16 shows the temperature biases, as compared indicates a small diurnal variation in the global radio- to radiosondes, as a function of the forecast length for sondes. It may be related to the fact that the 0–24–48–72– the different experiments. At 50 mb (upper left), MW 96–120-h forecasts only used 0000 UTC radiosonde data shows the largest biases for all the forecast times, while for verification, while the 12–36–60–84–108-h forecasts RO has the smallest bias. IR and CTL show similar only used 1200 UTC radiosonde data for verification. biases, and these are larger than in BASE and RO. This However, it is surprising that the global mean of all indicates that the assimilation of IR and MW increases radiosondes shows a variation between 0000 and the bias of the analyses and forecasts over BASE. Since 1200 UTC. CTL has a higher bias than RO, the number of RO observations is not enough to anchor the model and com- 5. Conclusions pletely correct the biases in the IR and MW satellite radiances. This study has investigated the individual impacts of Results are similar at 250 mb (upper right), although limb-viewing RO and nadir-viewing IR and MW ob- at this level IR, BASE, and CTL show similar biases, servations on analyses and forecasts for March 2013 in significantly larger than RO. As at 50 mb, the MW bias is the operational NCEP global model. We carried out five the largest. At 500 mb (lower left), as the forecast time experiments; the control uses all observations in the increases, the BASE experiment produces the worst fit, NCEP operational forecasts for March 2013. A BASE followed by MW. At this level, IR, RO, and CTL show experiment removes all satellite IR, MW, and RO ob- the least biases. At 850 mb (lower right), BASE, IR, servations. The next three experiments successively add CTL, and RO show a negative (cold) bias while the MW back the IR, MW, and RO observations separately.

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FIG. 16. Temperature (K) fit to radiosondes of CTL (purple), BASE (black), IR (blue), MW (green), and RO (red) forecasts as a function of the forecast time for different pressure levels. Bottom graphs in each panel show differences with respect to BASE. Bars show limits of statistical significance at the 95% confidence level; values above or below the bars are statistically significant.

When compared to radiosondes, all the analyses ex- the biases of all experiments are under 0.1 K. In the cept RO are too cold in the stratosphere (above 100 mb), lower troposphere, around 925 mb, all analyses show and this cold bias is mostly caused by the IR and MW a cold bias of about 20.1 K, possibly due to model errors observations. All experiments are warm compared to in resolving the location and structure of the atmo- radiosondes at 200 mb, likely a result of warmly biased spheric boundary layer. The MW experiment shows the aircraft observations. RO is only a little less biased than largest biases in these comparisons. The ﬁt of the speciﬁc the other experiments at this level. From 300 to 800 mb, humidity analyses to radiosondes shows a drier

Unauthenticated | Downloaded 09/23/21 07:17 PM UTC 4184 MONTHLY WEATHER REVIEW VOLUME 142 stratosphere and a moister troposphere for all experi- from UCAR for making helpful comments. We also ments, suggesting that the model is slightly biased. thank the National Space Office in Taiwan and NSF, We analyzed the similarities and differences between NASA, NOAA, the U.S. Air Force, and the U.S. Navy the impacts of IR, MW, and RO observations with re- for their support of COSMIC. We are also grateful to spect to the BASE experiment. The benefits of RO in Dr. Alexander MacDonald for providing computing anchoring the model were demonstrated, as the exper- resources to run the experiments. Three anonymous iments only assimilating satellite radiances show larger reviewers provided excellent comments. biases throughout the whole vertical range of the atmosphere. These biases are found to be larger in the stratosphere, where the impact of RO is the greatest. APPENDIX Overall, the three satellite systems produce similar impacts. The impacts are larger in the SH and over the Calculation of Means, RMS Differences, and oceans, where the number of nonsatellite observations is Correlation Coefficients much lower. RO produces the smallest impacts because of a. Mean differences the lower number of observations being assimilated compared to the number of IR and MW observations. RMS The variable A is a global field (analysis or forecast) of differences between the different experiment impacts some variable (e.g., T or q) and is a function of latitude l, show the greatest differences in the tropics and the SH. longitude f, time t, and height z: A(l, f, t, z). Although correlations between the analysis impacts For each experiment we have 32 eight-day forecasts at from the IR, MW, and RO experiments tend to be rel- t 5 0 (one per day, starting at 0000 UTC). From this set atively high and positive, they are surprisingly low in the of 32 analyses available for each experiment, we com- lower troposphere for both the temperature and mois- pute the following horizontal and temporal average: ture fields. There are even negative correlations in the 1 1 1 monthly mean impacts at some levels. However, A(z) 5 å A(l, f, t, z): N N N monthly means of the daily impacts are positively cor- lat lon time lat, lon, time related at all levels. The IR and MW impacts are more highly correlated than the IR and RO or MW and RO Then, the mean difference between the analyses of ex- impacts, suggesting the greater independence of RO periment 1 and the analyses of experiment 2 is computed A 2 A observations from the other two systems. as 1 2. This gives us a value of the mean difference Forecasts made from the five different analyses show for each vertical level. that the satellite observations make the largest positive b. RMS differences impact in the Southern Hemisphere and that IR, MW, A l f and RO observations produce similar positive impacts. Given the analyses of two experiments, 1 ( , , t, z) A l f A A In the Northern Hemisphere, the satellite observations and 2 ( , , t, z), the RMS different between 1 and 2 make a relatively small but statistically significant posi- is computed as follows: tive impact. IR and MW observations alone also make A A a small but significant positive impact, but only out to rms( 1 and 2) 5 days. The forecasts confirm previous studies that show " # RO acts as an anchor observing system, reducing the 1 1 1 5 sqrt å (A 2 A )2 . forecast biases in both the NH and SH. 1 2 Nlat Nlon Ntime lat, lon, time In summary, this study emphasizes again the com- plementary nature of IR, MW, and RO observations in This gives us an RMS value for each vertical level. increasing the forecast skill and producing forecasts with minimum biases. c. Correlation coefficient Given the analysis A (l, f, t, z), we compute the Acknowledgments. L. Cucurull was partially funded following temporal average: by the U.S. Disaster Relief Appropriations Act of 2013, Public Law 113-2 (the ‘‘Sandy Supplemental’’), project 1 At(l, f, z) 5 å A(l, f, t, z): ‘‘Establishment of a NOAA Laboratory Activity for Ntime time Observing System Simulation Experiments.’’ We thank Yanqiu Zhu and Jack Woollen from NCEP/EMC for The global spatial correlation between the temporally At At very productive discussions on the assimilation of radi- averaged analyses of two experiments 1 and 2 is ances and NCEP’s verification package and Ben Ho computed as follows:

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At At scorr( 1 and 2) 1 å [At (l, f, z) 2 A (z)][At (l, f, z) 2 A (z)] 2 f 1 1 2 2 g (N N ) 1 lat, lon 5 ( lat lon ) ( ), 1 1 sqrt å [At (l, f, z) 2 A (z)]2 sqrt å [At (l, f, z) 2 A (z)]2 2 1 1 2 2 2 (NlatNlon) 1 lat, lon (NlatNlon) 1 lat, lon

118, where A1 and A2 are the horizontal and temporal av- measurements. J. Geophys. Res. Atmos., 14–28, erages of the analyses of experiment 1 and the analyses doi:10.1029/2012JD017782. ——, R. A. Anthes, and L.-L. Tsao, 2014: Radio occultation of experiment 2, respectively. This gives us correlation observations as anchor observations in numerical weather coefficient value for each vertical level. prediction models and associated reduction of bias correc- tions in microwave and infrared satellite observations. J. Atmos. Oceanic Technol., 31, 20–32, doi:10.1175/ REFERENCES JTECH-D-13-00059.1. Dee, D. P., 2004: Variational bias correction of radiance data in the Anthes, R. A., 2011: Exploring Earth’s atmosphere with radio ECMWF system. Proc. Workshop on Assimilation of High- occultation: Contributions to weather, climate and space Spectral-Resolution Sounders in NWP, Reading, United King- weather. Atmos. Meas. Tech., 4, 1077–1103, doi:10.5194/ dom, ECMWF, 97–112. amt-4-1077-2011. ——, 2005: Bias and data assimilation. Quart. J. Roy. Meteor. Soc., Aparicio, J. M., and G. Deblonde, 2008: Impact of the assimilation 131, 3323–3343, doi:10.1256/qj.05.137. of CHAMP refractivity profiles in Environment Canada ——, and S. Uppala, 2008: Variational bias correction in ERA- 136, global forecasts. Mon. Wea. Rev., 257–275, doi:10.1175/ Interim. ECMWF Tech. Memo. 575, 26 pp. 2007MWR1951.1. Derber, J. C., and W.-S. Wu, 1998: The use of TOVS clouds-cleared Baker, N., and R. Daley, 2000: Observation and background ad- radiances in the NCEP SSI analysis system. Mon. Wea. Rev., joint sensitivity in the adaptive observation-targeting problem. 126, 2287–2299, doi:10.1175/1520-0493(1998)126,2287: 126, Quart. J. Roy. Meteor. Soc., 1431–1454, doi:10.1002/ TUOTCC.2.0.CO;2. qj.49712656511. Errico, R. M., 2007: Interpretation of an adjoint-derived observa- Ballish, B. A., and V. K. Kumar, 2008: Systematic differences in tional impact measure. Tellus, 59A, 273–276, doi:10.1111/ aircraft and radiosonde temperatures. Bull. Amer. Meteor. j.1600-0870.2006.00217.x. 89, Soc., 1689–1708, doi:10.1175/2008BAMS2332.1. Gelaro, R., and Y. Zhu, 2009: Examination of observation impacts Bauer, P., G. Radnóti, S. Healy, and C. Cardinali, 2014: GNSS derived from observing system experiments (OSEs) and radio occultation constellation observing system experiments. adjoint models. Tellus, 61A, 179–193, doi:10.1111/ Mon. Wea. Rev., 142, 555, doi:10.1175/MWR-D-13-00130.1. j.1600-0870.2008.00388.x. Bonavita, M., 2014: On some aspects of the impact of GPSRO ——, ——, and R. M. Errico, 2007: Examination of various-order observations in global numerical weather prediction. Quart. adjoint-based approximations of observation impact. Meteor. J. Roy. Meteor. Soc., doi:10.1002/qj.2320, in press. Z., 16, 685–692, doi:10.1127/0941-2948/2007/0248. Cardinali, C., 2009: Forecast sensitivity to observation (FSO) as Hajj, G. A., E. R. Kursinski, W. I. Bertiger, L. J. Romans, and K. R. a diagnostic tool. ECMWF Tech. Memo. 599, 26 pp. Hardy, 1994: Assessment of GPS occultations for atmospheric ——, and F. Prates, 2011: Performance measurement with ad- profiling. Preprints, Seventh Conf. on Satellite Meteorology vanced diagnostic tools of all-sky microwave imager radiances in 4D-Var. Quart. J. Roy. Meteor. Soc., 137, 2038–2046, and Oceonography, Monterey, CA, Amer. Meteor. Soc., doi:10.1002/qj.865. J7–J10. ——, and S. B. Healy, 2014: Impact of GPS radio occultation ——, ——, L. J. Romans, W. I. Bertiger, and S. S. Leroy, 2002: A measurements in the ECMWF system using adjoint-based technical description of atmospheric sounding by GPS occul- 64, diagnostics. Quart. J. Roy. Meteor. Soc., doi:10.1002/qj.2300,in tation. J. Atmos. Sol.-Terr. Phys., 451–469, doi:10.1016/ press. S1364-6826(01)00114-6. ——, L. Isaksen, and E. Andersson, 2003: Use and impact of au- Healy, S. B., 2008: Assimilation of GPS radio occultation mea- tomated aircraft data in a global 4DVAR data assimilation surements at ECMWF. GRAS SAF Workshop on Applica- system. Mon. Wea. Rev., 131, 1865–1877, doi:10.1175/2569.1. tions of GPSRO Measurements, Reading, United Kingdom, Collard, A. D., and S. B. Healy, 2003: The combined impact of ECMWF, 99–109. [Available online at www.romsaf.org/ future space-based atmospheric sounding instruments on nu- Workshops/agrom_prog/Healy.pdf.] merical weather prediction analysis fields: A simulation study. ——, 2013: Surface pressure information retrieved from GPS radio Quart. J. Roy. Meteor. Soc., 129, 2741–2760, doi:10.1256/ occultation measurements. Quart. J. Roy. Meteor. Soc., 139, qj.02.124. 2108–2118, doi:10.1002/qj.2090. Cucurull, L., 2010: Improvement in the use of an operational ——, and J-N. Thépaut, 2006: Assimilation experiments with constellation of GPS radio occultation receivers in weather CHAMP GPS radio occultation measurements. Quart. J. Roy. forecasting. Wea. Forecasting, 25, 749–767, doi:10.1175/ Meteor. Soc., 132, 605–623, doi:10.1256/qj.04.182. 2009WAF2222302.1. ——, A. M. Jupp, and C. Marquardt, 2005: Forecast impact ex- ——, J. C. Derber, and R. J. Purser, 2013: A bending angle forward periment with GPS radio occultation measurements. Geo- operator for global positioning system radio occultation phys. Res. Lett., 32, L03804, doi:10.1029/2004GL020806.

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Kelly, G., and J. Pailleux, 1988: Use of satellite vertical sounder data in Radnóti, G., P. Bauer, A. McNally, C. Cardinali, S. B. Healy, and the ECMWF analysis system. ECMWF Tech. Memo. 143, 46 pp. P. de Rosnay, 2010: ECMWF study on the impact of future Kuo, Y.-H., T.-K. Wee, S. Sokolovskiy, C. Rocken, W. Schreiner, developments of the space-based observing system on numer- D. Hunt, and R. A. Anthes, 2004: Inversion and error esti- ical weather prediction. ECMWF Tech. Memo. 638, 115 pp. mation of GPS radio occultation data. J. Meteor. Soc. Japan, Rennie, M. P., 2010: The impact of GPS radio occultation assimi- 82, 507–531, doi:10.2151/jmsj.2004.507. lation at the Met Office. Quart. J. Roy. Meteor. Soc., 136, 116– Kursinski, E. R., G. A. Hajj, K. R. Hardy, J. T. Schofield, and 131, doi:10.1002/qj.521. R. Linfield, 1997: Observing Earth’s atmosphere with radio Rocken, C., and Coauthors, 1997: Analysis and validation of GPS/ occultation measurements. J. Geophys. Res., 102, 23 429– MET data in the neutral atmosphere. J. Geophys. Res., 102, 23 465, doi:10.1029/97JD01569. 29 849–29 866, doi:10.1029/97JD02400. ——, ——, S. S. Leroy, and B. Herman, 2000: The GPS radio oc- Todling, R., 2013: Comparing two approaches for assessing ob- cultation technique. Terr. Atmos. Oceanic Sci., 11, 53–114. servation impact. Mon. Wea. Rev., 141, 1484–1505, Langland, R. H., and N. Baker, 2004: Estimation of observation doi:10.1175/MWR-D-12-00100.1. impact using the NRL atmospheric variational data assimila- WMO, 2012: Final report of the Fifth WMO Workshop on tion adjoint system. Tellus, 56A, 189–201, doi:10.1111/ the Impact of Various Observing Systems on Numerical j.1600-0870.2004.00056.x. Weather Prediction. WMO Tech. Rep. 2012-1, 25 pp. McNally, T., M. Bonavita, and J.-N. Thépaut, 2014: The role of [Available online at www.wmo.int/pages/prog/www/OSY/ satellite data in the forecasting of Hurricane Sandy. Mon. Wea. Meetings/NWP5_Sedona2012/Final_Report.pdf.] Rev., 142, 634–646, doi:10.1175/MWR-D-13-00170.1. Yang, F., 2013: Review of GFS forecast skills in 2013. NCEP Tech. Melbourne, W. G., and Coauthors, 1994: The application of Rep., 69 pp. [Available online at www.emc.ncep.noaa.gov/ spaceborne GPS to atmospheric limb sounding and global GFS/docs/GFS.performance.review.2013.pdf.] change monitoring. Tech. Rep. 94-18, Jet Propulsion Labo- Zhu, Y., J. Derber, A. Collard, D. Dee, R. Treadon, G. Gayno, ratory, Pasadena, CA, 158 pp. and J. A. Yung, 2013: Enhanced radiance bias correction in Poli, P., P. Moll, D. Puech, F. Rabier, and S. B. Healy, 2009: Quality the National Centers for Environmental Prediction’s control, error analysis, and impact assessment of FORMOSAT-3/ Gridpoint Statistical Interpolation data assimilation system. COSMIC in numerical weather prediction. Terr. Atmos. Oce- Quart. J. Roy. Meteor. Soc., 140, 1479–1492, doi:10.1002/ anic Sci., 20, 101–113, doi:10.3319/TAO.2008.01.21.02(F3C). qj.2233. ——, S. B. Healy, and D. P. Dee, 2010: Assimilation of Global Zou, X., L. Lin, and F. Weng, 2014: Absolute calibration of ATMS Positioning System radio occultation data in the ECMWF upper level temperature sounding channels using GPS RO ERA-Interim reanalysis. Quart. J. Roy. Meteor. Soc., 136, observations. IEEE Trans. Geosci. Remote Sens., 52, 1397– 1972–1990, doi:10.1002/qj.722. 1406, doi:10.1109/TGRS.2013.2250981.

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