Mesoscale GPS Zenith Delay assimilation during a Mediterranean heavy precipitation event

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1 Mesoscale GPS Zenith Delay assimilation during a Mediterranean heavy precipitation event Xin Yan, Veronique Ducrocq, Paul Poli, Geneviève Jaubert, ndrea Walpersdorf To cite this version: Xin Yan, Veronique Ducrocq, Paul Poli, Geneviève Jaubert, ndrea Walpersdorf. Mesoscale GPS Zenith Delay assimilation during a Mediterranean heavy precipitation event. dvances in Geosciences, European Geosciences Union, 2008, 17, pp /adgeo meteo HL Id: meteo Submitted on 17 May 2017 HL is a multi-disciplinary open access archive for deposit and dissemination of scientific research documents, wher y are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L archive ouverte pluridisciplinaire HL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

2 dv. Geosci., 17, 71 77, uthor(s) This work is distributed under Creative Commons ttribution 3.0 License. dvances in Geosciences Mesoscale GPS Zenith Delay assimilation during a Mediterranean heavy precipitation event X. Yan 1, V. Ducrocq 1, P. Poli 1, G. Jaubert 1, and. Walpersdorf 2 1 GME-CNRM, CNRS & Météo-France, 42 venue Coriolis, Toulouse Cedex 1, France 2 LGIT, Université Joseph Fourier, Maison des Géosciences, P 53, Grenoble Cedex 9, France Received: 31 March 2008 Revised: 11 June 2008 ccepted: 30 June 2008 Published: 29 July 2008 bstract. The impact of assimilating Zenith Total delay (ZTD) observations from a mesoscale ground-based GPS network over Western Europe is evaluated for heavy precipitation event of 5 9 September 2005 over Sourn France. The ZTD assimilation is performed using a three dimensional variational assimilation system at 9.5- km horizontal resolution. Then using as initial conditions 3DVR analyses with and without assimilation of ZTD, we perform 2.4-km non-hydrostatic MESO-NH simulations. The results of fine-scale simulations indicate that assimilation of ZTD help to improve forecast of tropospheric water vapour content and quantitative precipitation forecast. We have also assessed through single observation experiments influence of formulation of observation operator which is used to compute model equivalent ZTD. 1 Introduction Tropospheric water vapour is highly variable in space and time and, besides, is a key-ingredient for success of finescale heavy rainfall forecast (Ducrocq et al., 2002). Lack of high-resolution water vapour observations is one of considerable sources of inaccuracy in model analyses that are used as initial conditions of numerical wear prediction models (Kuo et al., 1996). The Zenith Total Delays (ZTD) deduced from GPS measurements are attractive for providing tropospheric water vapour in a context of an increasing number of ground-based GPS receivers. In present study, we examine impact of mesoscale ZTD assimilation on fine scale (2.4-km) forecast of a Mediterranean heavy precipitation event. The impact is eval- Correspondence to: X. Yan uated more specifically for 5 to 9 September 2005 period, during which several precipitating systems affected Souastern France. The cumulative surface rainfall during whole period was over 300 mm over a significant part of region, reaching locally more than 500 mm. Section 2 presents ZTD assimilation methodology using 9.5-km 3DVR LDIN assimilation system. Section 3 evaluates impact of formulation of observation operator based on a single observation assimilation experiment. Then, Sect. 4 discusses impact of assimilating GPS on high-resolution forecast of 5 9 September 2005 rainy event, based on non-hydrostatic 2.4-km MESO-NH simulations. The conclusions follow in Sect Description of ZTD assimilation 2.1 The 9.5-km 3DVR assimilation system The assimilation of ZTD is performed with LDIN 3DVR assimilation scheme, which has been running operationally at Météo-France since July 2005 (Fischer et al., 2005; Montmerle et al., 2007). To evaluate impact of assimilating ZTD GPS, two sets of six-hourly forecastanalysis cycles are run from 1st to 10th of September 2005 with 3DVR assimilation system. For first set, called hereafter CTRL, observations usually included in operational 3DVR LDIN are assimilated. The latter include observations from radio-soundings, screen-level stations, wind profilers, buoys, ships and aircraft. The following satellite are also assimilated: horizontal winds from atmospheric motion vectors (MVs) and Quickscatt scatterometers, dvanced Microwave Sounding Unit (MSU)- and - radiances and tmospheric dministration (NO)- 15, -16, -17 and QU satellites, High-resolution Infrared Sounder (HIRS) radiances from NO-17 and clear Published by Copernicus Publications on behalf of European Geosciences Union.

3 72 X. Yan et al.: GPS zenith delay assimilation X. Yan et al.: GPS zenith delay assimilation e sec- obser- S VP, h netivarais et al., 0 stailable is centation. et per plying called to acsed in center ussian e dift ZTD cts as ce beheight s with firste preround losest f GPS s (Fig. of un- ZTD ng r each -guess TD is alyses s one on opcodes, ed by not et luated ZTD. Fig. 1. Location in 3DVR LDIN domain of GPS stations of E-GVP and OHM-CV networks for September The pink-circled stations are those selected for assimilation. The - line indicates location of cross-sections shown in Fig.2. The 2.4-km MESO-NH domain is delineated by red box. POLI07 SEVIRI radiances integratesfrom METEOST-8 bottom to satellite. top of The model secondfollowing set, calledequation, GPS, assimilates using intotal addition pressure ZTD P, obser- tem- perature vations. T and partial pressure of water vapor e of atmospheric model column: 2.2 GPS ZTD P ZTD = (k 1 T + k e 3 )dz (1) In GPS assimilation T 2 cycle, we assimilate GPS arising from European GPS with k 1 = Pa 1 K, station network (E-GVP, k 3 = Pa 1 K and from a 32-stations research network deployed 2 (Smithwithin and Weintraub, Mediterranean 1953). Cévennes-Vivarais hydrometeorological The equation for REN06 Observatory is (OHM-CV, following: Delrieu et al., 2005). For studied P period, ZTD = (k 1 + k 2 e T v T + e from more than 450 stations covering all Western k 3 Europe T 2 )dz + were ZTD thustop available (2) (Fig. 1). with One k 2 GPS = station 6 can Pabe 1 K, processed k 3 = by severalpa analysis 1 K2, centers, = leading k 2 kto d 1 several and k 2 R R v (evis ZTD et al, time-series 1994); Tfor v being a given station. virtual temperature, For assimilation, R d = in orderj/(kmol.k) to have a uniform and R v set = per J/(kmol.K) station, we select specific only one molar solution gas constants per station forby dryapplying air and water pre-processing vapour, respectively. developed by Poli et al. (2007) (called hereafter REN06 POLI07), have evaluated slightlydifferent modifiedsets in order of (k 1 to, ktake 2, k 3 into ) coefficientcount proposed higher resolution in literature assimilation for ZTD values system computed used in ac- from our study. high-resolution The pre-processing (2.4km) selects non-hydrostatic pairs of station-center atmospheric simulated verifying that fields. The first-guess resultsdeparture showed that follows rea isgaussian no significant distribution differences (The first-guess in evaluation departureof isztd defined between as most difference coefficient between observed sets (withztd a mean and model bias ofequivalent ZTD lessztd than of 2computed mm), except fromfor 6-hour set using LDIN two-coefficient forecast whichformula, acts as which first-guess presentsina mean assimilation ZTD bias reaching cycle). The nearly difference 12 mm. be- tween station height and model ground surface height should also be less than 150 m. Station-center pairs with large time availability, small standard deviation of firstguess departure or most Gaussian distribution are preferred. Within assimilation window (±3 h around analysis time), GPS observation which is closest to analysis time is chosen. The pre-processing of GPS retains at end 262 stations out of 481 stations (Fig. 1). To ensure that observations meet hyposis of unbiased errors assumed in assimilation scheme, ZTD are bias-corrected before assimilation following method used by POLI07. The bias is computed for each station-center pair based on a 15-day average of first-guess departure between 15 and 31 ugust Sensitivity to observation operator To study impact of how model equivalent ZTD is calculated in assimilation system, we perform analyses where 3DVR LDIN system only assimilates one zenith delay observation using two different observation operators available, as ir tangent-linear and adjoint codes, in assimilation software. The first one has been used by POLI07 and second one has been proposed by renot et al. (2006) (called hereafter REN06) which had evaluated many expressions to calculate model equivalent ZTD. POLI07 integrates from bottom to top of model following equation, using total pressure P, temperature T and partial pressure of water vapor e of atmospheric model column: P ZTD = (k 1 T + k e 3 )dz (1) T 2 with k 1 = Pa 1 K, k 3 = Pa 1 K2 (Smith and Weintraub, 1953). The equation for REN06 is following: P ZTD = (k 1 + k e 2 T v T + e k 3 T 2 )dz + ZT D TOP (2) with k 2 = Pa 1 K, k 3 = Pa 1 K2, and k 2 = k R 2 k d 1 R v (evis et al, 1994); T v being virtual temperature, R d = J/(kmol.K) and R v = J/(kmol.K) specific molar gas constants for dry air and water vapour, respectively. REN06 have evaluated different sets of (k 1, k 2, k 3 ) coefficients proposed in literature for ZTD values computed from high-resolution (2.4 km) non-hydrostatic atmospheric simulated fields. The results showed that re is no significant differences in evaluation of ZTD between most of coefficient sets (with a mean bias of ZTD less than 2 mm), except for set using two-coefficient formula, which presents a mean ZTD bias reaching nearly 12 mm. ZTD contribution above model top level ZT D TOP is dv. Geosci., 17, 71 77,

4 4 X. Yan et al.: GPS zenith delay assimilation X. Yan et al.: GPS zenith delay assimilation 73 a) b) m % m % c) d) m % m % Fig. Fig. 2. Vertical 2. Vertical cross cross section section along along 670-kmline line - showninin Fig. 1 of analyses and analysis increments of of relative humidity, obtained obtained by by assimilation of aof single a single ZTD ZTD observation using REN06 (ac) or POLI07 (bd) observation operator. Panels ab ab display display relative relative humidity humidity from from 3DVR 3DVR analysis analysis and and panels panelscd cd analysis increment of relative humidity ( (analysis increment being being defined defined as as difference difference between between analysis analysis and and first-guess). The The vertical arrows in panels cd indicate location of of ZTD ZTDobservation. also added in REN06. POLI07 did not consider this constant correction in ir observation operator and left this to low model terrain. In that case, extrapolation of pressure, implementation, except when GPS station is located be- RMS is slightly weaker for MCTRL runs. Figure 5 Conclusions 5 shows Frequency ias (FIS, ougeault (2003)) for be handled by ir bias correction scheme. ZT D precipitation events with observed 12-h accumulated rainfall top in temperature and humidity values from model lowest level The impact of assimilating Zenith Total delay (ZTD) observations from a dense GPS network has been evaluated for REN06 is computed according to Saastamoinen (1972): down to GPS station altitude is needed. In that extrapolation procedure, temperature is considered as constant above 0.1 mm, 0.5 mm, 1 mm, 5 mm, 10 mm and 20 mm. FIS ZTindicates D wher experiment has a tendency to high-resolution forecast of heavy precipitation event of 5- TOP = 10 6 k 1R d P TOP (3) in POLI07 while REN06 assumes a constant temperature under-forecast (FIS <1) g TOP or to over-forecast (FIS>1) 9gradient. September 2005 over Mediterranean northwestern coasts. precipitation with P TOP events, and gwith TOP being a perfect score pressure being and1. Foracceler- ation of gravity MGPS at experiment top of shows model. an improved POLI07 skill eval- Figure have been 2 shows assimilated results of leads toassimilation a weak positive of aimpact single on all Using as initial conditions analyses in which GPS ZTD thresholds, compared uated to to 2.3 mm MCTRL, this constant especially contribution for higher above precipitation DIN/RPEGE events. FISmodel for toptwo at 1experiments hpa. indicate a tentivlent scale ZTD MESO-NH computed for model. first better guess forecast with REN06 of and model L- observation quantitative of ZTD precipitation of value forecasts m; issued model by equiva- convecdencyin to order under-forecast to perform events integration for weak of ZTD thresholds. operator inside model, POLI07 and REN06 follow nearly same are by also found. mm and 18.6 mm, respectively. Note that 2.3 mm equivalent POLI07 observation ZTD andoperators thus of departs tropospheric from observation water vapour The impact of formulation of observation operator has also been studied through dv. a Geosci., single observation 17, 71 77, 2008 experiment. Two slightly different observation operators showed

5 X. 74Yan et al.: GPS zenith delay assimilation X. Yan et al.: GPS zenith delay assimilation 5 a) IS (m) ll GPS stations in MesoNH domain MGPS MCTRL 0 P3 P6 P9 P12 P15 P18 Forecasting range (hour) Table Table IS IS and and RMS RMS for for ZTD ZTD simulated by by GPS GPS and and CTRL CTRL MESO-NH experiments against against observed observedztd. For For all allgps stations, stations, scores scoresare arecomputed computedgaring garingall all raw rawztd ZTDobserva- tionsover over MESO-NHdomain. For For only onlyassimilated assimilatedgps stations, stations, scores scoresare arecomputed computedonly onlyfor forztd ZTDvalues valuesused usedin in as- similation similationafter afterremoving removingof of station stationbias bias (see (seesection Sect. 2.1). The The tions scores scoresare arecomputed computedgaring garingall all 3-hourly 3-hourlyforecasts forecastsof ofall all MESO-NH MESO-NH18-h 18-hduration runs runs from from 12 12:00 UTC, UTC, 5 September 5 September to 18 to 18:00 UTC, UTC, 8 September 8 September all all GPS GPS Stations Stations only only assimilated GPS GPS Stations MCTRL MCTRL MGPS MGPS MCTRL MCTRL MGPS MGPS IS 23.1 mm 22.6 mm 14 mm 13.4 mm RMS 30.8 mm 30.6 mm 26.9 mm 26.2 mm RMS 30.8 mm 30.6 mm 26.9 mm 26.2 mm b) 0.03 ll GPS stations in MesoNH domain MGPS MCTRL Table 2. IS and RMS for 12-h accumulated precipitation forecast from 2. IS MGPS and RMS and MCTRL for 12-h accumulated MESO-NH experiments precipitationagainst fore- Table cast observed from precipitation. MGPS andthe MCTRL scoresmeso-nh are computing experiments garingagainst all observed 12-hourlyprecipitation. forecasts (i.e. The 0 12scores h andare 6 18 computing h forecasting garing ranges) all of all 12-hourly MESO-NH forecasts 00:00 (i.e. UTC 0-12h and and 12: h UTCforecasting runs covering ranges) period of all frommeso-nh 12:00 UTC, 00 UTC 5 September and 12 UTC 2005runs to 18:00 covering UTC, 8period September from UTC, 5 September 2005 to 18 UTC, 8 September RMS (m) MCTRL MGPS all GPS Stations IS 0.5 mm 0.3 mm IS 0.5 mm 0.3 mm RMS 19.7 mm 20.2 mm RMS 19.7 mm 20.2 mm P3 P6 P9 P12 P15 P18 Forecasting range (hour) Fig. 3. IS and RMS for model equivalent ZTD as function of forecast range for MGPS and MCTRL MESO-NH runs. Scores are computed against ZTD observations included in MESO-NH domain (see red box in Fig. 1).The scores are computed garing all 3-hourly forecasts of all MESO-NH 18-h duration runs from 12:00 UTC, UTC, 5 September 5 September to 18to UTC, 18:00 8 September UTC, 8 September that assimilation of ZTD modifies mainly low to midtroposphere moisture. The main difference between two (out of 18.6 mm difference) would normally be corrected operators lie in amplitude of relative humidity increments, only reflecting larger first-guess differences in one op- by a bias correction (as in POLI07) but we neglect this effect here. fter minimization of cost function in erator (some of se differences are explained by a constant assimilation, model equivalent ZTD analysis differs from model top contribution). lmost no difference is found for observation of only 1.47 mm for REN06 and of 1.9 mm vertical distribution of analysis increment of relative humidity in both cases. for POLI07, showing a good behaviour of assimilation with both operators. The main impact of assimilating ZTD is more seen comprehensive on humidity analysis field. There of is 3DVR almost LDIN no impact ZTD on assimilation pressure or temperature over whole field. September The horizontal 2005 month influence is range subject of of observation a companion is in paper. agreement Work with is in progress horizontal to assimilate correlation ZTD length of directly background-error at convective covariance scale using matrix a 2.5-km which is about assimilation 70 km in system middle and troposphere evaluate its for impact specific on humidity. We can see in Fig. 2cd that assimilation experiment quantitative using POLI07 precipitation yields an forecast. analysis increment finer scaleof model humid- is expected ity in to low- reduce to mid- troposphere height differences more intense betweenthan in GPS station assimilation and experiment model orography, using observation and refore operator improve from REN06. accuracy The oflarger observation analysis increments operator. imply more corrections to humidity field by analysis. To summarize, in cknowledgements. present example, The authors use ofwould different like operators thank can E-GVP lead to project about 2% for of ir difference efforts in inmaking final European analysisground-based increment ofgps humidity, available without in near however real time. modifying The authors vertical are grateful distribution to constructive of humidity comments analysis made increment. by Dr. Romu Romero. The OHM- CV GPS network and this study benefited from support from national research program LEFE, Institut National des Sciences de l Univers, and of European Commission through 4 Impact on fine scale forecasts FP6/PREVIEW project. The impact of ZTD assimilation on convective scale forecast of 5 9 September 2005 Mediterranean heavy References precipitation episode has been assessed, using two-way gridnestingm., MESO-NH S. usinger, domains S. Chiswell, at 9.5-km T.. Herring, and 2.4-km, R.. ns, respec- evis, tively. C. Rocken, Ducrocq and R.H. et al. Ware: (2002), GPSLebeaupin Meteorology: et Mapping al. (2006) Zenith and Nuissier Wet Delays et al. onto (2008) Precipitable provided Water, a comprehensive J. ppl. Meteor., description 33, 379- of386,1994. this MESO-NH model configuration, that was successfully used P.: for simulating The WGNEMediterranean survey of verification heavy precipitation methods for ougeault, events numerical in se prediction studies. of The wear 3DVR elements LDIN and severe analyses wear issued events. fromcs/jsc two assimilation WGNE Report,18, cycleswmo/td-no.1173, with and without ZTD ppendix C, 1-11, 2003 assimilation, as described in Sect. 2, are used as initial dv. Geosci., 17, 71 77,

6 X. Yan et al.: GPS zenith delay assimilation 75 Fig h accumulated precipitation (mm) from 00:00 UTC to 12:00 UTC, 6 September 2005 for: (a) MGPS MESO-NH run starting from GPS LDIN analysis at 00:00 UTC; (b) MCTRL MESO-NH run starting from CTRL LDIN analysis at 00:00 UTC; (c) raingauges; (d) differences between MGPS and MCTRL 12-h accumulated precipitation. conditions to MESO-NH simulations. Two 18-hourduration MESO-NH runs were issued every day at 00:00 and 12:00 UTC from 12:00 UTC, 5 September to 00:00 UTC, 8 September 2005, covering whole rainy period. The MESO-NH set using 3DVR LDIN analyses with ZTD assimilation is called MGPS hereafter, whereas or one is called MCTRL. We focus here on results of 2.4-km MESO-NH runs. The model equivalent ZTD at GPS stations have been computed for every 3-hourly forecasts from 3 to 18 h range issued from two sets of MESO-NH runs. Figure 3 shows bias and Root Mean Square error (RMS) computed against observed ZTD for all GPS stations included in 2.4-km MESO-NH domain (Fig. 1). Scores for MGPS runs show an improved forecast of ZTD for longer ranges (15 and 18-h ranges) compared to MCTRL. IS and RMS are quite similar for two MESO-NH sets for very shortrange (Fig.3). The IS and RMS computed over all 3-hourly forecasts, whatever forecasting ranges, lead to same conclusion of slightly better results for MGPS runs (Table 1, left columns). If scores are computed only on subset of GPS stations assimilated in 3DVR LDIN system (Table 1, right columns), MGPS runs are also found better. Note that IS is weaker on that dv. Geosci., 17, 71 77, 2008

7 76 X. Yan et al.: GPS zenith delay assimilation X. Yan et al.: GPS zenith delay assimilation FIS of 12H accumulated precipitations GPS CTL 1. For all thresholds, MGPS experiment shows an improved skill compared to MCTRL, especially for higher precipitation events. FIS for two experiments indicate a tendency to under-forecast events for weak thresholds mm 0.5mm 1mm 5mm 10mm 20mm Fig. 5. FIS for 12-h accumulated precipitation forecast from MGPS and MCTRL MESO-NH experiments against observed precipitation. FIS is computing garing all 12-hourly forecasts (i.e h h and hh forecasting ranges) of all MESO- NH 00:00 UTC UTC and 12 andutc 12:00 runs UTC covering runs covering period fromperiod 12 UTC, from5 12:00 September UTC, September to 18 UTC, September to 18:00 UTC, September , subset Nuissier, than O., on V. Ducrocq, whole D. GPS Ricard, -set C. Lebeaupin becauseand S. scores nquetin: are computed numerical on study biases-corrected of three catastrophic ZTDprecipitating values used events forover 3DVR Western assimilation. Mediterranean So, region using(sourn as initial France). conditions Part I: an Numerical inframework which ZTD and synoptic haveingredients, been assimilated Quart. J. improves Roy. Meteor. analysis forecast Soc., 134, of zenith , total delay and thus of tropospheric water vapour. Poli, P., P. Moll, F. Rabier, G. Desroziers,. Chapnik, L. erre, S.. Healy, E. ndersson, and F.-Z. EI Guelai: Forecast impact studies impactofofzenith assimilating total delaygps on from European high-resolution near real- The quantitative time GPS precipitation stations in Meteo forecast France is 4DVR, also found J. Geophys.Res., slightly positive. 112,D06114, Figure shows 12-h accumulated precipitation results Saastamoinen, of MGPSJ.: andintroduction MCTRL runs to practical issued from computation correspondingnomical GPS and refraction. CTRL ladin ull. Geod., analysis 106, at , :00 UTC, 6 Septem- of astrober Smith, E. andcompared S. Weintraub: with The constants raingauge equation (Fig. for atmo- could refractive see that MGPS index atrun radiolocates frequencies, betterproc. heavy IRE, 1035 pre- 4c), wespheric cipitation 1037, region over GRD department while MC- TRL run misplaces heavy precipitation area over OUCHES-DU-RHONE department. The differences reach more than 80 mm in some locations(fig. 4d) between se two runs (MGPS-MCTRL). Table 2 shows IS and RMS computed over every 12-h accumulated precipitation totals (i.e. 0 h 12 h and 6 h 18 h forecast ranges) for MGPS and MCTRL runs. Scores are computed against raingauge observations in 2.4-km MESO-NH domain, which counts about 220 raingauges. The MGPS set improves IS whereas RMS is slightly weaker for MCTRL runs. Figure 5 shows Frequency ias (FIS, ougeault (2003)) for precipitation events with observed 12-h accumulated rainfall above 0.1 mm, 0.5 mm, 1 mm, 5 mm, 10 mm and 20 mm. FIS indicates wher experiment has a tendency to under-forecast (FIS<1) or to over-forecast (FIS>1) precipitation events, with a perfect score being 5 Conclusions The impact of assimilating Zenith Total delay (ZTD) observations from a dense GPS network has been evaluated for high-resolution forecast of heavy precipitation event of 5 9 September 2005 over Mediterranean northwestern coasts. Using as initial conditions analyses in which GPS ZTD have been assimilated leads to a weak positive impact on quantitative precipitation forecasts issued by convective scale MESO-NH model. better forecast of model equivalent ZTD and thus of tropospheric water vapour are also found. The impact of formulation of observation operator has also been studied through a single observation experiment. Two slightly different observation operators showed that assimilation of ZTD modifies mainly low to midtroposphere moisture. The main difference between two operators lie in amplitude of relative humidity increments, only reflecting larger first-guess differences in one operator (some of se differences are explained by a constant model top contribution). lmost no difference is found for vertical distribution of analysis increment of relative humidity in both cases. more comprehensive analysis of 3DVR LDIN ZTD assimilation over whole September 2005 month is subject of a companion paper. Work is in progress to assimilate ZTD directly at convective scale using a 2.5-km assimilation system and evaluate its impact on quantitative precipitation forecast. finer scale model is expected to reduce height differences between GPS station and model orography, and refore to improve accuracy of observation operator. cknowledgements. The authors would like to thank E-GVP project for ir efforts in making European ground-based GPS available in near real time. The authors are grateful to constructive comments made by Romu Romero. The OHM-CV GPS network and this study benefited from support from national research program LEFE, Institut National des Sciences de l Univers, and of European Commission through FP6/PREVIEW project. Edited by:. Mugnai Reviewed by: R. Romero dv. Geosci., 17, 71 77,

8 X. Yan et al.: GPS zenith delay assimilation 77 References evis, M., usinger, S., Chiswell, S., Herring, T.., ns, R.., Rocken, C., and Ware, R. H.: GPS Meteorology: Mapping Zenith Wet Delays onto Precipitable Water, J. ppl. Meteor., 33, ,1994. ougeault, P.: The WGNE survey of verification methods for numerical prediction of wear elements and severe wear events, CS/JSC WGNE Report, 18, WMO/TD-NO.1173, ppendix C, 1 11, renot, H., Ducrocq, V., Walpersdorf,., Champollion, C., and Caumont, O.: GPS Zenith Delay Sensitivity evaluated from High-Resolution NWP Simulations of 8 9th September 2002 Flash-Flood over Souastern France, J. Geophys. Res., 111, D15105, doi: /2004jd005726, Delrieu G., Ducrocq, V., Gaume, E., Nicol, J., Payrastre, O., Yates, E., Kirstetter, P. E., ndrieu, H., yral, P.-., ouvier, C., Creutin, J.-D., Livet, M., nquetin, S., Lang, M., Neppel, L., Obled, C., Parent-du-Chatelet, J., Saulnier, G.-M., Walpersdorf,., and Wobrock, W.: The catastrophic flash-flood event of 8 9 September 2002 in Gard region, France: a first case study for Cévennes-Vivarais Mediterranean, Hydro-meteorological Observatory, J. Hydrometeorol., 6, 34 52, Ducrocq, V., Ricard, D., Lafore, J. P., and Orain, F.: Storm-Scale Numerical Rainfall Prediction For Five Precipitating events over France: On Importance of initial humidity field, Wear Forecast, 17, , Fischer, C., Montmerle, T., erre, L., uger, L., and Stefanescu, S. E.: n overview of variational assimilation in L- DIN/FRNCE NWP system, Q. J. Roy. Meteor. Soc., 131, , Kuo, Y.-H., Zou, X., and Guo, Y.-R.: Variational assimilation of precipitable water using a non-hydrostatic mesoscale adjoint model. Part I: Moisture retrieval and sensitivity experiments, Mon. Wear. Rev., 124, , Lebeaupin, C., Ducrocq, V., and Giordani, H.: Sensitivity of Mediterranean torrential rain events to sea surface temperature based on high-resolution numerical forecsats, J. Geophys. Res., 111, D12110, doi: /2005jd006541, Montmerle, T., Rabier, F., and Fischer, C.: Respective impact of polar orbiting and geostationary satellite observations in ladin/france NWP system, Q. J. Roy. Meteor. Soc., 133, , Nuissier, O., Ducrocq, V., Ricard, D., Lebeaupin, C., and nquetin, S.: numerical study of three catastrophic precipitating events over Western Mediterranean region (Sourn France). Part I: Numerical framework and synoptic ingredients, Q. J. Roy. Meteor. Soc., 134, , Poli, P., Moll, P., Rabier, F., Desroziers, G., Chapnik,., erre, L., Healy, S.., ndersson, E., and EI Guelai, F.-Z.: Forecast impact studies of zenith total delay from European near realtime GPS stations in Meteo France 4DVR, J. Geophys. Res., 112, D06114, doi: /2006jd007430, Saastamoinen, J.: Introduction to practical computation of astronomical refraction, ull. Geod., 106, , Smith, E. and Weintraub, S.: The constants in equation for atmospheric refractive index at radio frequencies, Proc. IRE, , dv. Geosci., 17, 71 77, 2008

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