401
The ECMWF Re-Analysis of the Mesoscale Alpine Programme Special Observing Period
Christian Keil and Carla Cardinali
Research Department
March 2003 For additional copies please contact
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Series: ECMWF Technical Memoranda
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c Copyright 2003
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The information within this publication is given in good faith and considered to be true, but ECMWF accepts no liability for error, omission and for loss or damage arising from its use. MAP Re-Analysis
Abstract At ECMWF, a reanalysis of the 70-day Special Observing Period (SOP) of the Mesoscale Alpine Programme (MAP) in autumn 1999 has been produced using the global assimilation system 4D-Var with a horizontal resolution of approximately 40 km (T511/159L60). Additional MAP observations used in the MAP reanal- ysis comprise e.g. European windprofilers, high-resolution radiosondes and surface observations. Four of the 16 European windprofilers reporting during the SOP had to be denied in the reanalysis due to their vari- able quality. The assimilation of the MAP observations leads to moister conditions in the southern Alpine region, southern France and over the Adriatic Sea while drier conditions prevail along the Alpine mountain chain. A data impact study shows that the windprofilers introduce changes in the divergent wind field which feeds back on the humidity analysis and slightly dries the troposphere in the southern Alpine region. The investigation on some IOPs illustrates some specific aspects of the MAP Re-Analysis. Finally, a comparison of GPS derived integrated water vapour contents and the analyzed counterparts shows that the analyzed hu- midity field well describes the inter-diurnal humidity variations at southern Alpine GPS stations. However, the analyses seem to be slightly too dry compared with GPS measurements.
1 Introduction
In meteorology, an analysis represents an image of the true state of the atmosphere at a given time. Analyses not only provide initial conditions for numerical weather forecasts but they can be used as a comprehensive diagnostic of the atmosphere or to check the quality of new observations. The Mesoscale Alpine Programme (MAP) Special Observing Period (SOP) took place from 7 September to 15 November 1999 and involved a large number of additional upper-air soundings and instrumented flights, along with an exceptional concentration of surface measurements (Bougeault et al., 2001). The use of the ECMWF 12-hour 4D-Var global assimilation system in conjunction with MAP observations provides a new, more accurate reference of the atmosphere at high resolution and with dense in-situ measurements in the Alpine region. The objectives of the MAP Re-Analysis are to:
produce a comprehensive set of analyses describing the state of the atmosphere for the 70-day period of MAP SOP in autumn 1999.
Create a formatted archive of the additional MAP observations.
Foster European and international research by making the observations and the analyses archive widely available.
Perform validation and diagnostic studies.
Indicate the benefit of the use of additional observations through data impact studies.
This report can be divided in two main parts: sections 2-5 provide a general description of the performance of the MAP Re-Analysis, while in sections 6 and 7 the quality of the MAP Re-Analysis is investigated for different Intensive Observation Periods (IOP). Aspects of the ECMWF Integrated Forecasting System (IFS) are provided in section 2 followed by an overview of the characteristics of MAP observations and their usage (section 3). A quality assessment of these additional observations is presented in section 4 before the general impact of the MAP-suite and the MAP observations is discussed in section 5. Case-study-type investigations of some IOPs in section 6 are complemented by an evaluation of the analyzed humidity field using GPS derived integrated water vapour measurements (section 7). After concluding remarks in section 8, the appendix contains more
Technical Memorandum No. 401 1 MAP Re-Analysis details of the quality of the European windprofilers, an overview of the MAP Re-Analysis products as well as the MARS retrieval template.
2 The Model System
Over the last years, a considerable improvement in the accuracy of forecasts from global numerical weather prediction (NWP) systems has been achieved. Since autumn 1999, when the MAP field experiment took place, there have been substantial modifications in the IFS (Simmons and Hollingsworth, 2002). The main model changes between the operational IFS in 1999 (cycle 21R2; denoted OPER’99 henceforth) and the version used for the MAP Re-Analysis (cycle 24R3; denoted MAP-suite henceforth) are briefly summarized. During the SOP on 12 Oct 1999, the number of vertical levels increased from 50 to 60, a new orography and associated subgrid orographic fields were introduced, and changes occurred in the cloud and convection schemes (Gregory et al., 2000). In 2000, other major changes involved an increase of the horizontal resolution from approximately 60 to 40 km (T319 to T511 spherical-harmonic representation), a revised treatment of the land surface scheme and a new parameterization of the long wave radiation. In the assimilation system, the 6-hour window 4D-Var has been extended to 12-hour (Bouttier, 2001a) and the inner-loop resolution increased from T63 to T159 (approximately 120 km horizontal resolution). Recently, a new shortwave radiation transfer model and new bias correction for satellite observations were included in the IFS. On the data usage, the improved version of RTTOV fast radiative transfer model (Matricardi et al., 2001) has facilitated a better use of satellite data in the MAP-suite, also neither SSM/I wind information nor brightness temperatures from the HIRS instrument were used in OPER’99 (see Tab. 1). Introduced in July 1999, dropson- des (Cardinali, 1999) and American windprofiler data (Bouttier, 2001b) have been used in OPER’99. Moreover, the increased analysis resolution allowed more use of aircraft data in the MAP-suite (Cardinali et al., 2003). Globally, these changes in the use of observations mainly affect the satellite sounding data (SATEM) resulting in an increase of more than 300 % while aircraft data use increases about 40 % in the MAP-suite. The addi- tional MAP observations account for a global increase of about 35 % for PILOTs (including pilot balloons and windprofilers), 26 % and 23 % for radiosondes and SYNOP stations, respectively. At the start of the MAP Re-Analysis Project at ECMWF, cycle 24R3 was created for the 12-hourly 4D-Var global assimilation system. This cycle became operational on 22 Jan 2002. Data of some satellite sounding instruments were available but not used in OPER’99, e.g. AMSU-A channel 14 which is sensitive in the stratopause region and ocean surface windspeed data from the SSM/I instrument. This data is used in the MAP Re-Analysis. With the new system version, some bias tuning was necessary to use 1999 satellite data.
Table 1: Difference in data usage between OPER’99 and the MAP-suite. Observation type Instrument Used Parameters OPER’99 MAP-suite SYNOP synop surface data RH2m, PS yes yes & MAP-data TEMP radiosondes u,v,T,q yes yes & MAP-data Europ. dropsonde u,v,T no MAP-data PILOT Europ. profiler u,v no MAP-data AIREP AMDAR etc. u,v,T yes more & MAP-data SATEM TOVS AMSU-A Tb yes more TOVS HIRS Tb no yes SSM/I windspeed no yes SSM/I TCWV yes more
2 Technical Memorandum No. 401 MAP Re-Analysis
6°E 8°E 10°E 12°E 14°E
48°N 48°N
46°N 46°N
44°N 44°N
6°E 8°E 10°E 12°E 14°E Figure 1: Location and colour-coded usage of the surface stations in the Alpine region: used (green), blacklisted due to ’peak’-effect (blue) and due to ’valley’-effect (red).
For instance, the mean bias of SSM/I total column water vapour (TCWV) used over oceans has been reduced to 0.79 kg m2, which is comparable to current operational values (OPER’99 shows larger bias amounting to 2.48 kg m2 for the same 5-day period in mid-September 99).
3 MAP observations
3.1 MAP Surface data
During the entire SOP, nearly 3 million observations of ¡ 10800 synoptic land stations have been archived. These surface stations were reporting pressure, temperature and humidity. However, only two observed values are assimilated: the relative humidity (RH2m) and the surface pressure (ps). Additionally, in the IFS there is another constraint on the usage of observations from synoptic land stations. If the station height is differing by more than 200 m from the corresponding model orography representation, the observations recorded at these synoptic stations are not representative and consequently discarded. In the MAP-suite, this prescribed threshold results in an exclusion of 40 % of all MAP surface stations, of which 28 % are declined because the model resolution is not resolving the Alpine valleys (’valley’-effect, ı.e. model height
- 200 m ¢ station altitude) and the rest 12 % because the Alpine mountain peaks are higher than the model
orography (’peak’-effect, ı.e. model height + 200 m £ station altitude; Fig. 1).
3.2 European windprofiler during MAP
European windprofiler data has only recently been incorporated in data assimilation schemes. Data quality was a significant issue before the data could be reliably used. Therefore, during the MAP-SOP no European windprofiler data distributed in real-time via GTS (Global Telecommunication System) were operationally assimilated in NWP models. At ECMWF, the monitoring of European windprofilers started in 2000 (Bout- tier, 2001b and Andersson and Garcia-Mendez, 2002). The assimilation of European windprofilers started on 9 April 2002 in the operational model suite (cycle 25R1), using 8 out of the 17 windprofiler stations from which data has been received.
Technical Memorandum No. 401 3 MAP Re-Analysis
Table 2: Available European windprofilers during the MAP SOP. The four stations in brackets have been denied in the MAP Re-Analysis. Station ID Latitude Longitude Name Country Elevation [m] Vertical Range 3501 52.42 -4.00 Aberystwyth UK 50 TROP/LST 3807 50.13 -5.10 (Camborne) UK 88 BL/TROP 3840 50.87 -3.23 (Dunkeswell) UK 253 BL/TROP 6348 51.95 4.88 Cabauw NL 0 BL 6792 46.47 9.72 Julier Pass CH 2233 BL 7112 48.61 0.87 La Ferte Vidame F 245 TROP/LST 7115 45.56 8.71 Lonate I 146 TROP/LST 7613 45.56 8.71 Lonate I 146 BL/TROP 7615 43.11 5.92 Toulon F 7 BL/TROP 7626 43.12 0.37 Lannemezan F 600 BL/TROP 7453 45.71 3.09 (Clermont) F 660 BL/TROP 10394 52,21 14.13 Lindenberg D 107 BL/TROP/LST 11105 47.18 9.36 Bad Ragaz CH 435 BL 11036 48.10 16.60 Vienna A 227 BL 11120 47.16 11.23 Innsbruck A 593 TROP 16228 42.40 13.40 (L’Aquila) I 1000 TROP
During the MAP SOP, 16 European windprofilers in 7 countries were active (Tab. 2). Altogether these stations reported 46620 atmospheric profiles of the wind components with different vertical and temporal resolution. The vertical range of the profilers differs from station to station depending on the frequency at which the radar instrument operates. Four of the 16 windprofilers detect the boundary layer (BL), 12 reach the mid- troposphere (TROP) and three profilers reach the lower stratosphere (LST). Many stations report twice hourly, while Aberystwyth reports every 8 minutes. Consequently, a temporal as well as vertical thinning of the data has been applied according to operational procedures, i.e. the 12 hourly 4D-Var window has been divided in 30-minute time-slots, within which only one profile per station is used. These half-hourly time-slots match well the temporal reporting frequency of most European windprofilers. Vertically, the profiles have been thinned to one observation every 5 hPa (or 5 % of pressure above 100 hPa). Since there has been no monitoring on the quality of European windprofilers available during the SOP, data of all stations has been used in a pilot analysis experiment. Having performed this analysis experiment for the first two weeks of the SOP, it became evident that data of some European windprofilers were deteriorating the quality of the analyses. Therefore, in the MAP Re-Analysis observations from four European windprofiler stations (Camborne, Dunkeswell, Clermont and L’Aquila) have been denied (see section B in appendix). Following current operational procedures, the observation error of windprofiler is equal to that of a radiosonde observation (see section 4).
3.3 High resolution radiosoundings
During the SOP, 5165 radiosonde measurements were flown from 20 European stations. Some were started at extra MAP radiosonde locations (e.g. Verona, Sterzing) while at numerous operational stations (e.g. Milano, Udine and Bologna) the radiosonde ascent frequency was increased (to 3 hourly). Some of the high resolution radiosondes were reporting data from more than 3000 levels per ascent. Since ECMWF’s assimilation system technically can deal with only 300 levels per ascent, the soundings were linearly thinned. Using a simple
4 Technical Memorandum No. 401 MAP Re-Analysis
a b c
Figure 2: Comparison of GTS data (black), high resolution SOP profile (green) and thinned SOP profile (red) for (a) relative humidity, (b) wind velocity and (c) direction at Milano on 17 Sep 99 12 UTC. thinning technique, evenly spaced data points have been selected from each high resolution profile. The thinned profiles closely follow the high resolution data set and show more vertical variability than the one transmitted via GTS (thinned by Vaisala method, WMO), as can be seen for the Milano station on 17 Sep 99
12 UTC (Fig. 2). The GTS profile shows 10 % maximum error for the relative humidity (Fig. 2a; 950 and
800 hPa) and 2 m/s for wind (Fig. 2b) with respect to both the higher resolution profiles.
3.4 Aircraft data and dropsondes
The eight research aircrafts deployed during the IOPs reported data along their flight path with very high temporal resolution. The amount of data gathered by these research aircrafts is similar to the one reported by commercial aircrafts across the Alpine region. However, their contribution to the overall assimilated aircraft data in the MAP-suite is negligible (less than 1 %) due to thinning procedures. Likewise, the amount of used dropsondes which were released from the research aircrafts during IOPs amounts to less than 1 % compared with radiosonde data.
4 Assessment of the quality of MAP observations
Standard deviation (STD) and bias of observation departures from the background (12-hour forecast from a previous assimilation cycle) and from the analysis are useful as they provide information to validate the assim- ilation performance. In particular, the decrease of STD of analysis departure with respect to the background departure indicates to which extent the assimilation model was able to fit different observation types. Recent work on the improvement in skill of NWP (Simmons and Hollingsworth, 2002) has shown that the short-range forecast error (1-day) has been reduced to the order of magnitude of the observation error. Large observation departures from the background would be then a sign of poor quality of the observations. Apart the MAP Re-Analysis (Exp-ID: e9mi; denoted MAP-RA henceforth, see Table 3), three other analysis experiments have been performed using the MAP-suite to assess the performance and the impact of MAP
Technical Memorandum No. 401 5 MAP Re-Analysis
Table 3: Overview of data usage in different analysis experiments using the MAP-suite: GTS denote data broadcast via the GTS, MDC the MAP observations provided by the MAP Data Centre (MDC). Experiment-Name ALDAT CNTRL NOPRO MAP-RA Experiment-ID e9cp e9ex e9jr e9mi Experiment period 7 -21 Sep 7 Sep-16 Nov 7 Sep-7 Oct 7 Sep-16 Nov Data type Used Parameters synop surface data RH2m, PS GTS + MDC GTS GTS + MDC GTS + MDC radiosondes u,v,T,q GTS + MDC GTS(6 h) GTS + MDC GTS + MDC Europ. dropsonde u,v,T MDC none MDC MDC Europ. profiler u,v all 16 profilers none none 12 profilers aircraft data u,v,T GTS + MDC GTS GTS + MDC GTS + MDC
observations. First, since there has been no monitoring of European windprofiler data in 1999, a pilot analysis experiment using all additional MAP observations has been carried out for two weeks in Sep 99 (Exp-ID: e9cp; denoted ALDAT henceforth). Second, a control experiment has been performed excluding all MAP observations for the entire SOP (Exp-ID: e9ex; denoted CNTRL henceforth). Third, an analysis experiment without the European windprofiler data has been completed for the first 30 days of the SOP to investigate their role in more detail (Exp-ID: e9jr; denoted NOPRO henceforth). The MAP-RA and CNTRL differ mainly in the usage of the MAP SOP observations. However, due to an error in the operational blacklist file, the American profilers were used in the control experiment in the whole troposphere, whereas in the MAP-RA only above 700 hPa (current corrected operational usage). Moreover, few dropsondes available next to tropical storms were neglected in CNTRL. During MAP, data of the increased ascent frequency at operational radiosonde stations (3 hourly) was transmitted via GTS and subsequently used in operational analysis. To exclude any additional MAP observations and replicate the routine ascent frequency of European radiosondes, only 6 hourly radiosonde data has been used in CNTRL. For the first two weeks of the MAP SOP, the background and analysis departures STD of experiment ALDAT have been calculated and compared with respect to CNTRL. These comparisons give evidence that some of the extra observations are deteriorating the analysis quality. The STD of e.g. the U-component of the wind of the observation types TEMP (radiosondes) and PILOT (pilotsondes) are larger for ALDAT than for CNTRL (Fig. 3a,b). PILOT departures show significant differences in the mid-troposphere. The deterioration of the radiosonde fit could be due to the extra radiosondes assimilated but also to some other observation types that measure wind. Apart from radio- and pilotsoundings, windprofilers are reporting information on the flow field at high temporal and vertical resolution. The standard deviation of the background departures of the European windprofilers peaks around 5 m s in the mid-troposphere (Fig. 3c) which is considerably larger than the values of radio- and pilotsoundings and well above the mean observation error (3 m s in the mid-troposphere, Tab. A.1). Also, there are more windprofiler measurements in the mid-troposphere than there are radio- and pilotsondes. Thus, the magnitude of STD of the background departures of the European windprofilers as well as the fit degradation in ALDAT with respect to CNTRL for radio- and pilotsondes suggest that some profilers are poor. In order to be able to identify the European windprofilers reporting poor quality measurements, observation statistics has been calculated individually for each station (see section B in appendix). Consequently, it has been decided to exclude four of the 16 European windprofilers in the MAP-RA, for which the bias is exceed- ing the predescribed observation error: Camborne (UK), Dunkeswell (UK), Clermont (F) and L’Aquila (I) (Fig. B.1 b,c,h and m). Omitting these four windprofilers results in a significant improvement: smaller stan- dard deviation of background and analysis departures for the remaining European windprofilers (Fig. 4c) and
6 Technical Memorandum No. 401 e9cp MAP SOP 1999090700-1999092100(6) background departure o-b(ref) background departure o-b EUprofiler-Uwind Europe analysis departure o-a(ref) used U analysis departure o-a STD.DEV BIAS 5 +0 0 5 10 +0 0 10 20 +0 0 20 30 +0 0 30 50 +353 353 50 70 +2170 2170 70 100 +6276 6276 100 150 +9038 9038 150 200 +7701 7701 200 250 +7850 7850 250 300 +11301 11301 300 400 +15648 15648 400 500 +26617 26617 500 700 +40229 40229 700 850 +36374 36374 850 1000 +5164 5164 1000 0 2 4 6 8 -6 -4 -2 0 2 4 6
background departure o-b(ref) e9cp MAP SOPbackground 1999090700-1999092100(6) departure o-b(ref) e9cp MAP SOP 1999090700-1999092100(6) background departure o-b EUprofiler-Vwindbackground Europe departure o-b TEMP-q Europe analysis departure o-a(ref) analysis departure o-a(ref) used q used V analysis departure o-a analysis departure o-a STD.DEV BIASSTD.DEV BIAS 300 +4271 10122 5 300 +0 0 5 10 +0 0 10 20 +0 0 20 400 +7680 18328 30 400 +0 0 30 50 +353 353 50 70 +2173 2173 70 500 +9702 25013 100 500 +6275 6275 100 150 +9039 9039 150 200 +7690 7690 200 background departure o-b(ref) 700 +10255 27927 e9mi 250 MAP SOP 1999090700-1999092100(6) 700 +7954 7954 250 300 +11338 11338 background departure 300 o-b MAP Re-Analysis EUprofiler-Uwind 400 Europe +15986 15986 analysis departure o-a(ref) 400 850 +7738 21809 500 850 +26477 26477 analysis departure 500 o-a used 700 U +40794 40794 700 850 STD.DEV +36598 36598 BIAS 850 1000 +2820 12831 1000 5 1000 +5148 +0 5148 0 1000 5 0 0.001 0.002 0.003 0.004 0.005 -0.0009-0.0006 0 10 00 0 2 0 40 6 8 +0 0 -6 -4 -2 0 2 4 6 10 20 +0 0 20 e9cp MAP SOP 1999090700-1999092100(6) e9cp MAP SOPbackground 1999090700-1999092100(6) departure o-b(ref) e9cp 30MAP SOPbackground 1999090700-1999092100(6) departure o-b(ref) +0 0 background departure 30o-b(ref) background departure o-b 50 background departure o-b -14 339 background departure 50 o-b TEMP-Uwind Europe PILOT-Uwindanalysis Europe departure o-a(ref) European 70 windanalysis profilerspeed departure o-a(ref) Europe -45 2125 analysis departure o-a(ref)70 100 -1175 5101 100 used U used U analysis departure o-a used 150 U analysis departure o-a -2752 6286 analysis departure 150 o-a 200 -1822 5879 200 STD.DEV BIASSTD.DEV BIAS250 STD.DEV -1569 6281BIAS OF SPEED 250 5 +435 1397 5 5 +2 208 300 5 5 -2586 +0 8715 0 300 5 10 +3517 8265 10 10 +6 521 400 10 10 -5336 +0 10312 0 400 10 20 +6122 13948 20 20 -10 536 500 20 20 -8083 +0 18534 0 500 20 30 +7390 18197 30 30 -9 514 700 30 30 -9068 +0 31161 0 700 30 50 +7352 18915 50 50 -14 544 850 50 50 -5970 +353 30404 353 850 50 70 +7582 18667 70 70 -144 1083 1000 70 70 +2170 -770 43942170 1000 70 100 +9166 21094 100 100 -766 2510 100 0 2 4 100 6 8 +6276 6276-6 -4 -2 0 2 4 6 100 150 +8069 20248 150 150 -61 3260 150 150 +9038 9038 150 200 +5462 16003 200 200 +46 2622 200 200 +7701 7701 200 250 +4347 11673 e9mi 250 MAP SOP 1999090700-1999092100(6) 250 -32 2002 e9mi 250 MAP SOPbackground 1999090700-1999092100(6) departure 250 o-b(ref) +7850 7850 background departure o-b(ref)250 300 +5910 13188 300 300 -2 2368 300 background departure 300 o-b +11301 11301 background departure 300 o-b 400 +7011 14790 TEMP-q 400 Europe 400 -94 2496 EUprofiler-Vwind 400 analysis departure Europe o-a(ref)400 +15648 15648 analysis departure o-a(ref) 400 500 +8862 18552 500 500 -77 2950 used 500 V analysis departure 500 o-a +26617 26617 analysis departure 500 o-a 700 +10136 23439 used 700 q 700 +78 3372 700 700 +40229 40229 700 850 +7886 22893 850 850 +23 2014 850 850 +36374 36374 850 1000 +2788 10658 1000 STD.DEV1000 +13 485 1000BIASSTD.DEV1000 +5164 5164 BIAS 1000 0 2 4 6 8 -6 -4 -2 300 00 2 2 4 46 6 8 -70 10052 -6 -4 -2 5 0 2 4 6 300 +0 0 5 a b c 10 0 2 4 6 8 +0 0 -6 -4 -2 0 2 4 6 10 20 +0 0 20 e9cp MAP SOP 1999090700-1999092100(6) e9cp 400 MAP SOPbackground 1999090700-1999092100(6) departure o-b(ref) -289 18039 30 background departure 400o-b(ref) +0 0 30 background departure o-b 50 background departure o-b -14 339 50 FTEMP-Vwindigure 3: T wo Europeweeks standard deviation ofPILOT-Vwindthe backgranalysis Europeound departure(o-b; o-a(ref) solid line) and analysis 70 (o-a;analysisdotted) departure o-a(ref)departures -45of 2128the 70 500 analysis departure o-a -270 24743 100 analysis departure 500 o-a -1169 5106 100 used V used V 150 -2733 6306 150 U-component of the wind for the observation types TEMP (a), PILOT (b) and European 200windprofiler (c) for ALD A-1813T. The5877 200 STD.DEV BIASSTD.DEV BIAS250 -1682 6272 250 5 +437 1398 700 5 5 -640 +2 27287 208 700 5 300 -2720 8618 300 refer ence 10 experiment is CNTRL +3520(blue). 8267 The number 10 s indicate the10 amount +6of 521data used with the difference 10 (blue numbers) 20 +6132 13958 20 20 -10 536 400 20 -5518 10468 400 showing 30 the excess of observations +7408 18214w.r.t. CNTRL. 850 30 30 -379 -9 21430 514 500 85030 -7792 18685 500 50 +7349 18913 50 50 -14 544 700 50 -9270 31524 700 70 +7614 18697 70 70 -146 1083 850 70 -6292 30306 850 1000 -251 12580 1000 1000 -703 4445 1000 100 +9167 21098 100 0 0.001 0.002 0.003 100 0.004 0.005 -771 2511-0.0009-0.0006 0 00 0 2 0 40 100 6 8 -6 -4 -2 0 2 4 6 150 +8054 20204 150 150 -62 3263 150 200 +5495 16027 200 200 +65 2652 200 e9mi 250 MAP SOP 1999090700-1999092100(6) +4305 11617 e9mi 250 MAP SOPbackground 1999090700-1999092100(6) departure 250 o-b(ref) -17 2038 e9mi MAP SOPbackground 1999090700-1999092100(6) departure 250o-b(ref) background departure o-b(ref) 300 +5817 13042 300 background departure 300 o-b +2 2398 background departure 300 o-b background departure o-b TEMP-Uwind 400 Europe +6965 14748 PILOT-Uwind 400 analysis Europe departure o-a(ref)400 -31 2619 European windanalysis profilerspeed departure o-a(ref)400 Europe analysis departure o-a(ref) 500 +8947 18667 500 500 -125 2908 500 used 700 U +10201 23504 used 700 U analysis departure 700 o-a +58 3423 used U analysis departure 700 o-a analysis departure o-a 850 +7955 23010 850 850 +35 2032 850 1000 STD.DEV +2846 10731 1000BIASSTD.DEV1000 +17 493 BIASSTD.DEV1000 BIAS OF SPEED 5 0 2 4 6 8 -15 1382 -6 -4 -2 5 00 2 2 4 46 5 6 8 -3 205 -6 -4 -2 5 0 2 4 6 5 +0 0 5 10 -111 8154 10 10 -9 512 10 10 +0 0 10 20 -244 13704 20 20 -10 526 20 20 +0 0 20 e9cp 30MAP SOP 1999090700-1999092100(6) -326 17871 30 background departure o-b(ref)30 -8 506 30 30 +0 0 30 50 -406 18509 50 background departure 50 o-b -10 534 50 50 -14 339 50 TEMP-T 70 Europe -375 18292 70 analysis departure o-a(ref) 70 -23 1060 70 70 -45 2125 70 used 100 T -465 20629 100 analysis departure 100 o-a -43 2467 100 100 -1175 5101 100 150 -287 19961 150 150 -66 3194 150 150 -2752 6286 150 200 STD.DEV -250 15753 BIAS200 200 -54 2568 200 200 -1822 5879 200 250 5 +369-144 11529 1238 250 250 5 -5 1997 250 250 -1569 6281 250 300 10 +3747 -158 13030 7221 300 30010 -21 2347 300 300 -2586 8715 300 400 20 +6160 -206 1131014584 400 40020 +51 2547 400 400 -5336 10312 400 500 30 +7644 -188 1437818364 500 50030 -4 2946 500 500 -8083 18534 500 700 50 +7762 -594 1467422845 700 70050 -51 3321 700 700 -9068 31161 700 850 70 +7803 -385 1476122508 850 85070 -43 1971 850 850 -5970 30404 850 1000 -229 10429 1000 1000 -10 475 1000 1000 -770 4394 1000 100 0 2 4 6 8 +9469 17447 -6 -4 -2 00 2 2 4 46 100 6 8 -6 -4 -2 0 2 4 6 a 150 +8409 17632 b 150 c 0 2 4 6 8 -6 -4 -2 0 2 4 6 200 +6425 15493 200 e9mi 250 MAP SOP 1999090700-1999092100(6) +5514 12886 e9mi MAP SOPbackground 1999090700-1999092100(6) departure 250o-b(ref) background departure o-b(ref) 300 +7278 15774 background departure 300 o-b background departure o-b TEMP-Vwind 400 Europe +8608 19168 PILOT-Vwindanalysis Europe departure o-a(ref)400 analysis departure o-a(ref) Figur 500e 4: As Fig. 3 but for MAP-RA +11561 26790 (black line). The reference 500 experiment is ALDAT (blue). The numbers indicate the used 700 V +12187 30100 used V analysis departure 700 o-a analysis departure o-a amount 850 of data used with the dif +8543fer 22708ence (blue numbers) showing 850 the excess of observations w.r.t. ALDAT. 1000 STD.DEV +3288 14013 BIASSTD.DEV1000 BIAS 5 0 1 2 3 4 -22 1376 -3 -2 -1 5 0 1 2 3 5 -3 205 5 10 -115 8152 10 10 -9 512 10 20 -245 13713 20 20 -10 526 20 30 -340 17874 30 30 -8 506 30 50 -388 18525 50 50 -10 534 50 70 -410 18287 70 70 -23 1060 70 reduced 100 standard deviation with -491 20607respect to 100ALDAT for TEMP 100 and -42PILO 2469 T observations, Fig. 4 100a and b, respec- 150 -341 19863 150 150 -65 3198 150 200 -222 15805 200 200 -74 2578 200 tively 250. The windprofiler standard -72 11545deviation 250of the background 250 departures -52 1986 is less than 4 m/s and 250comparable to 300 -187 12855 300 300 -58 2340 300 the magnitude400 of the assigned -267 observ14481 ation 400error (Fig. 4c). 400 The amount -14 2605of windprofiler data used 400 is reduced by 500 -273 18394 500 500 +96 3004 500 700 -561 22943 700 700 -34 3389 700 30 % 850compared to ALDAT. -462 22548 850 850 -45 1987 850 1000 -255 10476 1000 1000 -15 478 1000 0 2 4 6 8 -6 -4 -2 00 2 2 4 46 6 8 -6 -4 -2 0 2 4 6 Calculated for the full SOP, observation statistics for TEMPs and European windprofilers are similar, with the e9mi MAP SOP 1999090700-1999092100(6) background departure o-b(ref) background departure o-b standardTEMP-T Europedeviation of the background departuresanalysismeandering departure o-a(ref) around 3 m s and peaking at 300 hPa at the jet streamused T level (Fig. 5). The bias of the departuresanalysisof bothdepartureobserv o-a ation types remains close to zero. Similarities STD.DEV BIAS in statistics 5 between TEMPs -12and 1226 European windprofilers 5 confirm the equal weight given in the assimilation 10 -134 7087 10 20 -216 11094 20 process 30 to both the observation -316 14062types. The amount of used 30TEMPs is outnumbering the European windprofilers, 50 -344 14330 50 with 70even more TEMPs contrib -327 14434uting in the upper troposphere 70 and lower stratosphere. In the MAP-RA, about 100 -448 16999 100 150 -357 17275 150 40 % 200of the available windprofiler -334 15159 data has been assimilated, 200 with considerable differences in usage between 250 -256 12630 250 300 -290 15484 300 the sites:400 81 % of Lindenber g-296 reports18872 has been used, 74 %400 at Lonate and only 34 % at Aberystwyth (due to its 500 -500 26290 500 very 700high reporting frequenc y).-573 29527 700 850 -368 22340 850 1000 -144 13869 1000 Focussing0 on1 humidity2 3 observ4 ations,-3 -2the-1 additional0 1 2 MAP3 high resolution radiosondes increase the usage of
TEMP humidity data in MAP-RA by a factor of 4 compared to CNTRL in the Alpine region (43-49 N and
2-17 E). Generally, the bias of background and analysis departures is small (less than 0.3 g/kg; not shown). In the boundary layer below 700 hPa, CNTRL shows a negative bias, i.e. CNTRL is slightly too moist with
Technical Memorandum No. 401 7 e9mi MAP SOP 1999090700-1999111600(6) background departure o-b EUprofiler-Uwind Europe analysis departure o-a used U STD.DEV BIAS 5 2 5 10 0 10 20 0 20 30 0 30 50 4898 50 70 14238 70 100 31434 100 150 40573 150 200 35474 200 250 39039 250 300 50331 300 400 62692 400 500 98081 500 700 137365 700 850 121536 850 1000 17023 1000 0 2 4 6 8 -6 -4 -2 0 2 4 6
e9mi MAP SOP 1999090700-1999111600(6) background departure o-b EUprofiler-Vwind Europe analysis departure o-a used V STD.DEV BIAS MAP Re-Analysis 5 2 5 10 0 10 20 0 20 e9mi 30MAP SOP 1999090700-1999111600(6) 0 30 50 4908 background departure 50 o-b TEMP-Uwind 70 Europe 14256 70 100 31446 analysis departure 100 o-a used 150 U 40700 150 200 35575 200 250 STD.DEV 39303 BIAS 250 300 5 50448 8765 300 5 400 10 6272841422 400 10 500 20 9853472340 500 20 700 30 137954102466 700 30 850 50 120341110552 850 50 1000 70 107587 16919 1000 70 100 0 2 4 6 8 116981 -6 -4 -2 0 2 4 6 100 150 110500 150 200 89210 200 e9mi 250 MAP SOP 1999090700-1999111600(6) 70178 background departure 250 o-b 300 79668 300 European 400 wind profilerspeed Europe 90057 400 500 117510 analysis departure 500 o-a used 700 U 145669 700 850 132268 850 1000 STD.DEV 58520BIAS OF SPEED1000 a 5 0 2 4 6 8 2 -6 -4 -2 0 2 4 6 5 10 0 10 20 0 20 e9mi 30MAP SOP 1999090700-1999111600(6) 0 background departure 30 o-b 50 4898 50 TEMP-Vwind 70 EuropeEurop. windprofiler 14238 70 100 31434 analysis departure 100 o-a used 150 V 40573 150 200 35474 200 250 STD.DEV 39039 BIAS 250 300 5 50331 8766 300 5 400 10 6269241437 400 10 500 20 9808172399 500 20 700 30 137365102559 700 30 850 50 121536110675 850 50 1000 70 107651 17023 1000 70 b 100 0 2 4 6 8 117131 -6 -4 -2 0 2 4 6 100 150 110588 150 200 89178 200 250 70130 250 Figure 5: Standard de300viation and bias of the background (o-b; 79358solid line) and analysis (o-a; dotted) 300departures of (a) the 400 89584 400 U-component of the 500wind for the observation types TEMP 118477and (b) European windprofiler of the MAP-RA 500 for the entire 700 145130 700 SOP. The numbers indicate 850 the amount of data used. 131998 850 1000 58426 1000 0 2 4 6 8 -6 -4 -2 0 2 4 6 respect to observe9miations. MAP AboSOPv 1999090700-1999111600(6)e 700 hPa, the bias is close to zero showing a backgroundgood agreement departure o-bof CNTRL and TEMP-T Europe radiosonde observations. For MAP-RA, the bias of background departure isanalysisabout departure0.1 g/kg o-ahigher than in used T CNTRL, resulting in less moistSTD.DEVconditions below 700 hPa and slightlyBIASdrier ones aloft. However, the bias of the analysis departure 5 of MAP-RA is smallest throughout 8682 the troposphere, i.e. MAP-RA 5agrees better with 10 37513 10 observations than CNTRL 20 (not shown). 59891 20 30 80066 30 50 84960 50 Also, data of the additional 70 MAP surface stations contrib 84489ute to a large increase in the usage 70of humidity obser- vations in the Alpine 100 region (Tab. 4). These surface observ 99699 ations multiply the usage in MAP-RA 100 by a factor 150 99029 150 of 5 (MAP-RA vs 200OPER’99), while the higher resolution 88499of the MAP-suite accounts for 200an increase of only 250 77030 250 15 % (CNTRL vs OPER’99).300 Both the higher horizontal 95890resolution and the increased density 300 of surface obser- 400 116257 400 vations lead to a more 500 efficient use of SYNOP RH2m 163757measurements (30 % of all surface data500 have been used 700 178929 700 in MAP-RA, 25 % 850in CNTRL and 22 % in OPER’99). 130494Still, the majority of surface data is 850rejected due to the 1000 73414 1000 mismatch of the station0 height1 and the2 corresponding3 4 model-3orography-2 -1 representation0 1 2 in 3mountainous regions.
Table 4: Some statistics on surface humidity observations used in different analyses for an Alpine domain bounded by 43-49 N and 2-17 E. total data used data background departure (o-b) analysis departure (o-a) mean [%] RMS [%] mean [%] RMS [%] OPER’99 398704 88574 4.02 12.0 2.01 8.95 CNTRL 398681 101494 3.03 11.1 1.83 8.8 MAP-RA 1657472 483853 2.89 12.5 2.14 11.8
8 Technical Memorandum No. 401 MAP Re-Analysis
The decrease of the mean and the RMS of the analysis departure with respect to the background departure is an indication of the favourable influence these observations have in the analysis.
5 Assimilation and model diagnostics
For many MAP case-studies mesoscale model simulations have been performed using ECMWF operational analyses as initial and boundary conditions (e.g. Richard et al., 2003 and Volkert et al.,2003). Thus, the following comparisons of the ’new’ MAP-RA with the ’old’ OPER’99 allow for an estimation of the overall differences between both analyses, while the comparisons of MAP-RA with CNTRL and NOPRO enable an assessment of the impact of additional MAP observations.
5.1 Analysis increments of geopotential height
Analysis increments are calculated by subtracting the background field from the analysis. Small RMS analysis increments are a sign of consistency of the short range forecast with the observations. In Fig. 6, the differences of RMS of analysis increments for the geopotential at 500 hPa are presented. First, the MAP-suite has every- where smaller increments than OPER’99 (bluish colours in Fig. 6a). This positive impact of the MAP-suite becomes most obvious across the Atlantic and Northern Africa, regions which benefit from the better use of satellite data. Second, the additional MAP observations have a negligible influence on the 500 hPa geopoten- tial (Fig. 6b; values of less than 2 m, which is below the contour threshold in Fig. 6a). In terms of analysis increments of geopotential, the impact of the MAP-suite is clearly exceeding the impact of additional MAP observations. Similar impact has been seen for the other model variables. Diff in RMS of an-Incr: z 500 RMS(an_e9mi- bg_e9mi) - (an_0001- bg_0002) period 19990907-to-19991114 Diff in RMS of an-Incr: z 500 RMS(an_e9mi- bg_e9mi) - (an_e9ex- bg_e9ex) period 19990907-to-19991114 nh-mean e9mi=5.1 nh-mean e9mi=5.1 nh-mean rms-0002=7.7 nh-mean rms-e9ex=5.1 Europe-mean e9mi=5.5 Europe-mean e9mi=5.5 Europe-mean rms-0002=8.2 Europe-mean rms-e9ex=5.3 0.8 0.8
0.7 0.7
0.6 0.6
0.5 0.5
0.4 0.4
0.3 0.2
0.2 0.1 -0.2 -0.1
-0.3 -0.2
-0.4 -0.4
-0.5 -0.5
-0.6 -0.6
-0.7 -0.7 a b -0.8 -0.8
Figure 6: Differences of RMS of analysis increments for the geopotential [dam] at 500 hPa between (a) CNTRL and OPER’99 and (b) MAP-RA and CNTRL calculated for the SOP.
Technical Memorandum No. 401 9 MAP Re-Analysis
mean diff TCWV (e9mi- 0001) period 19990907-to-19991116 mean diff q 850 (e9mi- 0001) period 19990907-to-19991116 4.2 0.8 3.6 0.6 3 0.4 2.4 1.8 0.2 -0.2 1.2 0.6 -0.4 -0.6 -0.6 -1.2 -1.8 -0.8 -2.4 -1 -3 -1.2 -3.6
a -4.2 b -1.4
Figure 7: Mean analysis differences of (a) the total column water vapour [kg m2] and (b) specific humidity [g kg] at 850 hPa between MAP-RA and OPER’99 averaged over the entire SOP.
5.2 Analysis differences of integrated water vapour and specific humidity
Now we focus on the humidity distribution of the different analyses. The mean analysis differences of the vertically integrated (total column) water vapour (IWV) show predominantly more humid conditions in MAP- 2 RA than in OPER’99 (Fig. 7a). Locally, the differences are generally less than 1 ¡ 2kg m , corresponding to about 7 % of the mean integrated water vapour content over Europe (17kg m2 for the SOP). However, in mountainous regions like the Alpine crest and Corsica regions MAP-RA is slightly drier than OPER’99. In the boundary layer, the spatial distribution of the mean humidity differences resemble the IWV patterns, slightly moister across northwestern Europe and the Mediterranean Sea and slightly drier across the Alps and the Balkan (Fig. 7b, specific humidity difference at 850 hPa). These drier conditions across mountainous regions are partly caused by the increased model resolution resulting in a higher model orography in the MAP- suite than in OPER’99. The influence of MAP observations becomes evident in the analysis differences in Fig. 8. First, we compare MAP-RA with CNTRL. The IWV differences (Fig. 8a) as well as the differences of specific humidity in the boundary layer (Fig. 8b) show that MAP-RA is moister across France and Italy. Particularly, across southeastern France and the adjacent Gulf of Lion, MAP-RA is more than 1kg m2 IWV moister than CNTRL, with the largest differences in the lower levels (exceeding 1g kg for the specific humidity at 925 hPa, not shown). The moister conditions in the Po valley region are noteworthy (see section 6.2). Across the western Mediterranean Sea close to Sardinia and along the French-German border MAP-RA is drier than CNTRL. Which role did European windprofiler play? To answer this question, an additional analysis experiment was performed for the first month of the SOP excluding all European windprofiler information only (NOPRO).
In Fig. 8c,d the mean humidity differences between MAP-RA and NOPRO are displayed. Generally, the
2 ¡ differences are small, amounting to less than 0 ¡ 6kg m for the IWV content and less than 0 3g kg for the specific humidity in the boundary layer. Obviously, the European windprofilers are hardly responsible for the large differences between the MAP-RA and CNTRL in the Gulf of Lion region. However, windprofilers seem to dry the troposphere in the southern Alpine region and to moisten the Adriatic Sea and southern Italy (Fig. 8d). This feature is also present in Fig. 8b which confirms the windprofilers influence in MAP-RA over these areas. In summary, MAP observations lead to moister conditions in the Po valley region, despite the fact that the additional wind observations provided by European windprofilers tend to dry the troposphere in this region. Also, a moister humidity field can be seen across southern France and the Adriatic Sea.
10 Technical Memorandum No. 401 MAP Re-Analysis
mean diff TCWV (e9mi- e9ex) period 19990907-to-19991006 mean diff q 850 (e9mi- e9ex) period 19990907-to-19991006 1.5 0.7
0.6 1.2 0.5
0.9 0.4 0.3 0.6 0.2
0.1 0.3 -0.1 -0.3 -0.2
-0.6 -0.3
-0.4 -0.9 -0.5
a -1.2 b -0.6
mean diff TCWV (e9mi- e9jr) period 19990907-to-19991006 mean diff q 850 (e9mi- e9jr) period 19990907-to-19991006 0.9 0.6
0.5 0.6 0.4
0.3 0.3 -0.3 0.2
-0.6 0.1 -0.1
-0.9 -0.2 -0.3
-1.2 -0.4
-0.5 -1.5 -0.6
c -1.8 d -0.7
Figure 8: Mean analysis differences between MAP-RA and CNTRL (a,b) and between MAP-RA and NOPRO (c,d) of the total column water vapour [kg m2] (a,c) and specific humidity [g kg] at 850 hPa (b,d) averaged over one month starting on 7 Sep 99.
5.3 Forecast impact
In terms of forecast impact, MAP-RA is better than CNTRL after day 3. The geopotential anomaly correlation scores at different pressure levels (850, 500 and 200 hPa), verified against the own analysis and radiosonde observations indicate a slight positive impact over North Atlantic, North America and North Pacific (not shown). However, it was not the prime objective of MAP to improve the forecast. In fact, MAP observations only supplement the existing observing system, which is already very dense across Central Europe.
Technical Memorandum No. 401 11 MAP Re-Analysis
6 Results on some specific IOPs
After investigating the quality of ECMWF’s precipitation forecast during the full SOP, we focus on some special IOPs in this section. Additional IFS experiments have been performed for specific periods to complement the investigations and allow for an estimation of the impact of certain observation types available during the SOP and used in the MAP-RA.
6.1 Precipitation in the Po Catchment
Daily precipitation (24 hour accumulations from 06 UTC onwards) in the southern Alpine Region, an area comparable with the catchment of the Po river (7 - 12 E and 45 - 46.5 N), are presented here. Observed precipitation values for validation are taken from high resolution (25 km) analyses of daily Alpine rain-gauge observations embracing roughly 5000 rain-gauges (Frei and Haller¨ , 2001; Frei and Schar¨ , 1998). The forecast
rainfall is based on the model forecast started at 12 UTC (i.e. 18 - 42 h forecast range). Figure 9 depicts the time series of daily precipitation averaged over the Po catchment area (66.000 km2) for observations, OPER’99, CNTRL and MAP-RA, respectively. The agreement of the forecasts with the precipitation observations is remarkable. Peak values are found for IOP2b (20 Sept 99; day 13 in Fig. 9) recording 58 mm/day, IOP8 (21 Oct 99, day 44) and IOP15 (6 Nov 99, day 60) recording 30 mm/day. Generally the timing of the events is well represented in both suites. The forecast based on OPER’99 is performing well by capturing the majority of the events recording more than 10 mm area averaged precipitation.Time seriesForecasts of fromMAP-SOPMAP-RA sho precipitationw a better agreement averagedwith observ withinations, e.g.theon Podays catchment8 and 40 the spurious rainfall predicted by the operational forecast is not present in MAP-RA. During the first month forecasts from CNTRL are similar to the ones of MAP-RA, however, the peak values of the heavy precipitation events in the latter half of the SOP are overestimated. The observed mean daily precipitation of 5 mm/day is reproduced by forecasts from MAP-RA, while forecasts from CNTRL overestimate the mean amount by 4 % and operational forecasts by 10 %.
IOP2b
IOP8 IOP15
Figure 9: Time series of daily precipitation averaged over the Po catchment in the southern Alpine region. Rainfall data
are taken from IFS forecasts initialized at 12 UTC and a forecast range of ¡ 18 - 42 h.
12 Technical Memorandum No. 401 MAP Re-Analysis
6.2 IOP2a: 17 September 1999
During IOP2a on 17 Sep 99, a well defined squall-line crossed the Lago Maggiore area, where seven ground- based research radars provided detailed observations. Numerical simulations of this convective event have been performed with the Mesoscale Model Meso-NH by using a threefold nesting technique with horizontal mesh- sizes of 32, 8 and 2km (Lascaux et al., 2003). The reference experiment initialized with OPER’99 analysis on 17 Sep 99 12 UTC succeeds reasonably well to initiate the convective line over the Alpine foothills and to reproduce its propagation towards the East (Fig. 10c,d). However, initializing the Meso-NH Model with the MAP-RA, convection is almost entirely inhibited in the mesoscale simulation (Fig. 10e,f). In the following we focus on the analyzed humidity field to detect, for this episode, the failure of convection
in the mesoscale simulation. The analysis difference of specific humidity (Fig. 11) shows that the MAP-RA at
850 hPa is 5g kg drier than CNTRL in the southern Alpine region centered around 10 E. This is opposite the earlier finding that on average the MAP-RA is slightly moister in this region (comp. Fig. 8b).
The lack of humidity becomes apparent in the zonal vertical cross sections along 45.7 N slicing the south-
ern foothills of the Alps at 12 UTC (Fig. 12). Just east of Milano, between 9 and 11 E, the region were the squall-line occurs, there is a dry tongue of air with less than 6g kg specific humidity extending down to 900 hPa (Fig. 12a). This dry region, extending well into the boundary layer, is neither present in CNTRL nor in OPER’99 (Fig. 12b,d). However, there are differences in the low-level humidity content between OPER’99 and CNTRL: moister conditions near Milano and drier conditions across the Adriatic Sea in OPER’99. The difference between MAP-RA (Fig. 12a) and NOPRO (Fig. 12c) is caused by the use of windprofiler data in the MAP-RA which tend to dry the troposphere (see section 5.2). The differences are largely confined between 9
and 11 E, close to the location of the Lonate windprofilers (45.6 N, 8.7 E). To highlight the moisture evolution during the afternoon of 17 Sep, when the squall-line was sweeping across the Lago Maggiore area, the observed Milano radiosonde is compared with corresponding analyzed humidity profiles in Fig. 13. At 12 UTC, the time the mesoscale simulation has been initialized, the humidity profile of OPER’99 is too wet for the entire troposphere. The observed profile (in black thinned high resolution profile is depicted) shows high values (more than 10g kg) below 950 hPa and relatively low ones (less than 5g kg) around 800 hPa. In the following hours (18 UTC) the humidity has risen up to 900 hPa, while the dry layer around 800 hPa decreased. 6 hours later, just after the squall-line has passed Milano releasing heavy precipitation, the specific humidity in the mixed boundary layer amounts to 8g kg. The analyzed humidity fields do not reflect the observed profile. While OPER’99 is generally too moist (green line), the MAP-RA (red line) fails to reproduce the correct humidity profile. In fact, at 12 UTC, the humidity is too low below 850 hPa and at 18 UTC the dry profile has been extended throughout the boundary layer below 750 hPa. In Fig. 14a, the time-height diagramme of vertical motion valid at Lonate (the nearby location of two windpro- filers) is depicted. Between 12 and 21 UTC, subsidence encompasses the entire troposphere (1Pa s at 700 hPa at 15 UTC) in the MAP-RA. In CNTRL, the subsidence is limited to the lower troposphere (Fig. 14b) and it decreases later in the evening (21 UTC). In NOPRO, descending motion is present between 12 and 18 UTC in the lower troposphere, like in CNTRL. However, enhanced vertical motion develops after 18 UTC, with subsidence in the mid-troposphere at first followed by upward motion in the lower troposphere (Fig. 14c). Intercomparison of the humidity profiles and the vertical motion fields between the MAP-RA, CNTRL and NOPRO demonstrates once again the impact of windprofiler, in particular on the humidity field. Throughout the afternoon of 17 Sep, the MAP-RA is considerably drier than NOPRO (Fig. 13). The Lonate windprofilers have modified the wind field by leading to enhanced subsidence.
Technical Memorandum No. 401 13 MAP Re-Analysis
a b
c d
e f
Figure 10: Radar observed (a,b) and Mesoscale Model forecast (c-f) precipitation fluxes at 21 UTC (a,c,e) and 23 UTC 2 (b,d,f) on 17 September 99 across the Lago Maggiore Area in northwestern Italy (domain size 250 250 km ). The Meso- NH has been initialized at 12 UTC with OPER’99 (c,d) and with MAP-RA (e,f). The forecast precipitation fluxes and wind arrows depict model fields 2000 m above ground (Meso-NH images by courtesy of E. Richard).
14 Technical Memorandum No. 401 MAP Re-Analysis
AN(19990917 12UTC) e9mi vs e9ex q(850hPa) 8
7
540 6 5
4 564 552 3
564 2
564 1 0.5 -0.5 -1 Figure 11: Analysis difference of spe-
576 -2 cific humidity at 850 hPa between the 576 576 -3 MAP-RA and CNTRL (colour coded) -4 superimposed with 500 hPa geopoten- -5 tial height at 12 UTC on 17 Sep 99.
Cross section of spec hum 19990917 1200 step 0 Expver e9mi Cross section of spec hum 19990917 1200 step 0 Expver e9ex 500 500 10.57 11.74
550 550 2 2 2 2 600 600 2 2 2 650 4 4 650 4 2 10 10 4 700 700 4 4 4
750 750 4 6 6
6 6 6 800 6 800 6
8 8 8 8 850 850 6
8 900 8 900
950 950 10
1000 6 1000 6 5OE 6OE 7OE 8OE 9OE 10OE 11OE 12OE 13OE 14OE 15OE 5OE 6OE 7OE 8OE 9OE 10OE 11OE 12OE 13OE 14OE 15OE a 45.7N b 45.7N
Cross section of spec hum 19990917 1200 step 0 Expver e9jr Cross section of spec hum 19990917 1200 step 0 Expver 1 500 500 10.62 10.47
550 2 550 2
2 600 600 2 4 2 2 650 2 650 4 10 4 10 4 4 700 700
4 4 4 6 750 6 750
6 6 800 8 800 6
8 8 8 850 8 850 8
8 900 8 900
950 950
1000 6 1000 6 5OE 6OE 7OE 8OE 9OE 10OE 11OE 12OE 13OE 14OE 15OE 5OE 6OE 7OE 8OE 9OE 10OE 11OE 12OE 13OE 14OE 15OE c 45.7N d 45.7N
Figure 12: Vertical cross-section of specific humidity along 45.7 N at 12 UTC on 17 Sep 99 for (a) MAP-RA, (b) CNTRL, (c) NOPRO and (d) OPER’99.
Technical Memorandum No. 401 15 MAP Re-Analysis
a b c
Figure 13: Intercomparison of humidity soundings for Milano at (a) 12 UTC, (b) 18 UTC and (c) 00 UTC on 17 and 18 Sep 99, respectively: observation (black), OPER’99 (green), MAP-RA (red), CNTRL (light blue), NOPRO (dark blue) and experiment eci5 (magenta).
12.0 Pa/s 12.0 Pa/s
10.5 3 10.5 3
9.0 1 9.0 1
7.5 0.3 7.5 0.3
0.1 0.1 6.0 6.0 -0.1 -0.1 Height [km] 4.5 Height [km] 4.5 -0.3 -0.3 3.0 3.0 -1 -1
1.5 1.5 -3 -3
0.0 0.0 a 0 3 6 9 12 15 18 21 24 b 0 3 6 9 12 15 18 21 24 time time
12.0 Pa/s 12.0 Pa/s
10.5 3 10.5 3
9.0 1 9.0 1
7.5 0.3 7.5 0.3
0.1 0.1 6.0 6.0 -0.1 -0.1 Height [km] 4.5 Height [km] 4.5 -0.3 -0.3 3.0 3.0 -1 -1
1.5 1.5 -3 -3
0.0 0.0 c 0 3 6 9 12 15 18 21 24 d 0 3 6 9 12 15 18 21 24 time time
Figure 14: Time-height diagramme of the vertical motion at the location of the windprofiler Lonate (45.6 N, 8.7 E) for (a) MAP-RA, (b) CNTRL, (c) NOPRO and (d) experiment eci5 on 17 Sep 99. Note that descent is colour-coded yellowish-red and ascent greenish-blue.
16 Technical Memorandum No. 401 MAP Re-Analysis
A coupling between mass and humidity fields in 4D-Var can be induced by the model dynamics but also through the specific humidity background error standard deviation calculation. Operationally, an empirical formula computes at every cycle the humidity standard deviation as a function of background temperature and relative humidity. Modifications of the wind field due to windprofilers affect the temperature and via that formula also the humidity field. To prove that, we performed an experiment in which a constant humidity background error standard devia- tion was used instead of being computed by the empirical formula. For IOP2a, this special analysis experiment (Exp-ID: eci5) was started with MAP-RA initial conditions. The same European windprofilers were assimilated as in MAP-RA. Comparison between MAP-RA and eci5 analyzed humidity profile at Milano shows remarkable differences (red and magenta lines in Fig. 13) while the vertical motion field has hardly changed (Fig. 14a,d). In both cases the assimilation of the Lonate windprofiler enhances subsidence in the Lago Maggiore region. However, this has different consequences on the humidity resulting in considerably moister conditions in the experiment using a constant humidity background error standard deviation. Evidently, the descending motion indirectly feeds back on the humidity field through variations in the humidity background error standard devi- ation (MAP-RA; dry conditions) but not directly through adiabatic drying (experiment eci5; moist conditions). For IOP2a, the errorneous feed back causes the low humidities.
6.3 IOP2b: 20 September 1999
As we have already seen in the rainfall time series for the Po catchment (Fig. 9), IOP2b constitutes the heaviest precipitation event recorded during the SOP. While the forecast daily rainfall accumulation integrated over the
Observed daily precipitation on 20 Sep 1999 (averaged onto T511) IOP2b: 24 hourly precipitation (e9mi: 1999-09-19 12UTC +18..42h) mm mm 48 48 100 100
75 75
50 50 46 46
20 20
10 10 no data 44 5 44 5 a b 6 8 no data 12 14 6 8 12 14 10 1 10 1
IOP2b: 24 hourly precipitation (T319: 1999-09-19 12UTC +18..42h) IOP2b: 24 hourly precipitation (e9ex: 1999-09-19 12UTC +18..42h) mm mm 48 48 100 100
75 75
50 50 46 46
20 20
10 10
44 5 44 5 c d 6 8 12 14 6 8 12 14 10 1 10 1
Figure 15: Daily accumulated precipitation for IOP2b (20 Sep 99, 6 UTC - 6 UTC): (a) analyzed precipitation based on gauge observations and projected on T511 grid, (b) T511 forecast starting from MAP-RA, (c) operational T319 forecast from OPER’99 and (d) T511 forecast from CNTRL.
Technical Memorandum No. 401 17 MAP Re-Analysis
Sunday 19 September 1999 12UTC ECMWF Forecast t+21 VT: Monday 20 September 1999 00UTC 925hPa u-velocity/v-velocity Sunday 19 September 1999 12UTC ECMWF Forecast t+12 VT: Monday 20 September 1999 00UTC Surface: geopotential height Sunday 19 September 1999 12UTC ECMWF Forecast t+21 VT: Monday 20 September 1999 00UTC Model Level 46 cloud liquid water content 4°E 6°E 8°E 10°E 12°E 14°E 16°E 16°E dBZ 50.57
45 48°N 18
10
36 10 36 18
18 46°N 18 10
10 36 27
10
36 44°N 10 18
10
18
a 18 b 10 4°E 6°E 8°E 10°E 12°E 14°E Figure 16: Observed and synthetic radar reflectivity on 20 Sep 99: (a) Alpine Radar Composite depicting observed instantaneous radar reflectivities at 8:30 UTC and (b) synthetic radar reflectivity 2000 m above ground (same colour coding) collocated with the low level wind field in 925 hPa valid at 9 UTC (1909199912 + 21h).
Po catchment area hardly differs from precipitation data (58.7 mm observed, 56.7 mm forecast from OPER’99 and 54,7 mm forecast from MAP-RA; Fig. 9), there are differences in the rainfall’s spatial distribution. In Fig. 15a, the daily accumulated precipitation from the gauge based precipitation analysis (Frei and Haller¨ , 2001) projected onto the model grid is displayed. Strongest rainfall peaking in more than 100 mm was recorded in the Lago Maggiore area, in the Dolomites and Carnic Alps in northeastern Italy. Generally, intense precipitation
with daily accumulations of more than 75 mm was observed in a 100 km broad belt along 46 N at the southern foothills of the Alps. A zone of stronger precipitation along the southern foothills of the Alps is recognisable in all forecasts, in particular in the T511 forecasts. Forecast from OPER’99 misses the strong rainfall in the Lago Maggiore area but includes the precipitation maxima close to the Dolomites (Fig. 15c). Forecast from CNTRL reproduces the heavy precipitation exceeding 100 mm in the Dolomites and in the Lago Maggiore area (Fig. 15d), while MAP-RA predicts precipitation maxima in the Lago Maggiore area and the Bergamese Alps (Fig. 15b), both in good agreement with observations. The high rainfall values in northeastern Italy are missed by MAP-RA forecast. Unfortunately there are no humidity radiosoundings available in this region (e.g. Udine) to investigate this forecast failure. All forecasts overestimate the precipitation north of the Alps by exceeding 10 mm in Bavaria. Here the lee-effect of the Alps is underestimated. Furthermore, MAP-RA reproduces the ‘dry’ area East of the Alps Maritime in Liguria, confirmed by the high resolution analysis. The collocation of an observed radar image with its synthetic counterpart highlights to which degree of realism it is possible to match qualitatively individual precipitation systems using forecast fields of the IFS global model (Fig. 16). The synthetic radar reflectivities are calculated with a simple formula, which uses fields of rain water, cloud water and ice to construct radar echoes (Fovell and Ogura, 1988). Visualized by the wind field at 925 hPa, the moisture-laden cyclonic airflow is impinging on the Appennines
and the Alps, where at the luv-slopes high rainrates exceeding 10 mm/h ( ¢ 36 dBZ) at 2000 m are forecast
(Fig. 16b). The strongest rainrates with more than 30 mm/h ( ¢ 45 dBZ) are predicted at the southern Alpine slopes, where the flow in an unstable atmosphere is orographically forced to rise over the mountain chain. One branch of the precipitation system extends northwestward across Switzerland into France. The general features of this precipitation system can be identified in the radar observations (Fig. 16a), i.e. the cyclonic curl of the rainfall area south of the Alps and its extension towards France. However, the remotely-sensed precipitation intensity hardly exceeds 10 mm/h. The precipitation area upstream of the Appennines is not within the range of the Italian radars.
18 Technical Memorandum No. 401 MAP Re-Analysis
6.4 IOP8: 21 October 1999
On 21 Oct 99, another heavy rainfall event occurred in the southern Alpine region. During IOP8, the daily precipitation is quite uniformly distributed throughout the Po catchment area and the adjacent Alpine foothills amounting to 20-50 mm (Fig. 17a). Heaviest precipitation with maxima exceeding 50 mm were recorded in the area of Milano. All model forecasts, T319 operational forecast and T511 forecasts from MAP-RA and CNTRL, reproduce the widespread rainfall surmounting 20 mm south of the Alps. The higher resolution forecasts from MAP-RA and CNTRL predict high precipitation accumulations for the Milano region (Fig. 17b,d) well match- ing the observed pattern. However, all forecasts overestimate the precipitation over the western Appennines. The spurious rainfall peak predicted by the T511 forecast from CNTRL in the foothills of the Dolomites, which leads to the overestimation of the mean rainfall averaged over the Po catchment (see Fig. 9), is removed in MAP-RA. Medina and Houze (2003) showed, that the strikingly different precipitation patterns of IOP2b and IOP8 cor- respond with different flow regimes: unblocked flow up and over the mountains during IOP2b and flow around or blocked during IOP8. Although forecasts from MAP-RA do not agree in every way with precipitation data, they do capture the main difference between IOP2b and IOP8 (Fig. 15a,b and Fig. 17a,b, respectively) and so, document the capability of the model to reproduce the different mesoscale flow situations.
Observed daily precipitation on 21 Oct 1999 (averaged onto T511) IOP8: 24 hourly precipitation (e9mi: 1999-10-20 12UTC +18..42h) mm mm 48 48 100 100
75 75
50 50 46 46
20 20
10 10 no data 44 5 44 5 a b 6 8 no data 12 14 6 8 12 14 10 1 10 1
IOP8: 24 hourly precipitation (od: 1999-10-20 12UTC +18..42h) IOP8: 24 hourly precipitation (e9ex: 1999-10-20 12UTC +18..42h) mm mm 48 48 100 100
75 75
50 50 46 46
20 20
10 10
44 5 44 5 c d 6 8 12 14 6 8 12 14 10 1 10 1
Figure 17: Daily accumulated precipitation for IOP8 (21 Oct 99, 6 UTC - 6 UTC): (a) precipitation analysis based on gauge observations projected on T511 grid, (b) T511 forecast from MAP-RA, (c) operational T319 forecast from OPER’99 and (d) T511 forecast from CNTRL.
Technical Memorandum No. 401 19 MAP Re-Analysis
6.5 IOP15: 6/7 November 1999
In contrast to the previously discussed cases, we focus on dynamical features for IOP15, though 30 mm/day area-averaged precipitation is well captured by MAP-RA. This case constitutes a good example of upper-level features, which were observed with ground-based and airborne remote-sensing instruments deployed during the MAP campaign. On 6 Nov, a trough was elongating meridionally from the North Sea to the Mediterranean Sea. The following day, this trough crossed the Alps forming a cut-off low at its southern end above Italy. Based on NWP guidance, a flight pattern was planned (DLR-Falcon aircraft) to measure cross-streamer curtains of water vapour along this tropopause fold on 6 Nov. With the on-board H2O-Dial instrument, low humidities in the lower stratosphere and upper troposphere were measured applying a technique described in Ehret et al. (1999). Fig. 18a shows very dry stratospheric air intruding the troposphere, marked by low humidity values of less than 50 ppmv reaching down to approximately 7 km altitude between 15 and 16 UTC. A corresponding image of reanalyzed humidity is given in Fig. 18b, displaying the humidity in a vertical cross-
section along 45 N extending from Bordeaux to Venice at 15 UTC. Very low humidities of 50 ppmv extend down to 600 hPa (corresponding to 4 km altitude), giving evidence of the tropopause fold and the intrusion of dry stratospheric air. A closer inspection allows to detect mesoscale similarities between H2O-Dial observation
and the MAP-RA; whereas the 40 ppmv isoline drops vertically in the upper troposphere by about 3 km at the
western flank of the intrusion (at 3 E), it slants with an angle of about 45 at its eastern flank (at 4-8 E). A comparably good agreement of Dial observation and corresponding analysis is discernible neither in CNTRL nor in OPER’99 analyses (not shown). Observations of ground-based remote-sensing instruments are presented in Fig. 19 using time-height dia- grammes of horizontal wind velocity. The measurements of the VHF (Fig. 19a) and UHF (Fig. 19b) sensors of the Lonate windprofilers are displayed using a tiling method which enables the depiction of the actually measured data without any field smoothing. The length of the individual tiles represents the frequency of the measurements reported by the windprofilers, with 1 hourly data from VHF Lonate and quarter-hourly data from UHF Lonate. Data sparse areas are also easy to identify. The corresponding analysis and background wind fields of MAP-RA at Lonate are displayed using the same time-height representation (Fig. 20).
Cross section of spec hum 19991106 1500 step 0 Expver e9mi 200 100 km 0.01 0.03 0.1 0.03 90 300 0.01 0.1 10 0.3 80 0.03 0.3 400 0.1
0.1 70
0.3 1 500 0.3 8 1 60 2 600 0.1 50 3 0.3 40 2 2 6 700 4 2 1 30 3 a 0 10 20 30 40 50 60 70 80 90 100 200 300 400 500 800 3 4 20 3 4 4 5 C01 DLR D I AL wa t e r v apo r m i x i ng r a t i o p r o f i l e s ( ppmv ) f a991106 . r f 4 6 C02 Mean f l i gh t a l t i t ude : 11848m, mean t r ue a i r s peed : 216m/ s 5 C03 La s e r wa v e l eng t h : 935 . 684nm 900 10 C04 Re s o l u t i on o f smoo t hed da t a f i e l d : 18000m ho r i z on t a l , 500m v e r t i c a l C05 C06 b 6 C07 7 3.231 C08 1000 C09 0O 2OE 4OE 6OE 8OE 10OE 12OE C10 19991106 da t e 45.0N 599 numbe r o f p r o f i l e s 171 numbe r o f da t a po i n t s pe r p r o f i l e 53048 . 03 s t a r t t i me ( UTC , s ) 57600 . 30 s t op t i me ( UTC , s ) 44 . 9995 s t a r t l a t i t ude ( deg ) 45 . 0670 s t op l a t i t ude ( deg ) Figure 18: - 1 . 48Section29 s t a r t l ong i t uofde ( dloweg ) water vapour concentration across a tropopause fold; (a) data (in ppmv) obtained with an
11 . 8868 s t op l ong i t ude ( deg )
10645 . 00 s t a r t he i gh t (m) 5545 . 00 s t op he i gh t (m) airborne H 27 .O-Dial61 mean t i me i nlookingt e r v a l ( s ) down from 11 km. The flight segment is along 45 N from Bordeaux (1 W) to Venice (12 E) 1645 . 97 mean ho r i z on t a l g r i d d i s t an c e (m) 30 . 00 v e r t i c a l g r i d d i s t an c e (m) between 0 . 015000E+and000 dummy16- v a l ueUTC on 6 Nov 99. Collocated is a vertical cross-section of MAP-RA humidity at 15 UTC along 45 N
ASC I I F i l e : H2O_991106S . a s c (b; isolinesPS F i l e : of Hspecific2O_991106S . p s humidity in g/kg).
20 Technical Memorandum No. 401 MAP Re-Analysis
14 14 m/s m/s
12 12 40 40
35 35 10 10 30 30 8 8 25 25
6 6 20 20 Height [km] Height [km]
15 15 4 4
10 10 2 2 5 5
0 0 1 1 a 0 3 6 9 12 15 18 21 240 3 6 9 12 15 18 21 24 time time 6 Nov 99 7 Nov 99
14 14 m/s m/s
12 12 40 40
35 35 10 10 30 30 8 8 25 25
6 6 20 20 Height [km] Height [km]
15 15 4 4
10 10 2 2 5 5
0 0 1 1 b 0 3 6 9 12 15 18 21 240 3 6 9 12 15 18 21 24 time time
Figure 19: Time-height diagramme of horizontal wind velocity during IOP15 as observed by (a) VHF and (b) UHF windprofiler at Lonate.
The most prominent feature during IOP15 is the passage of the tropopause fold and the associated jet stream, which is crossing Lonate on 7 Nov. The wind in the upper troposphere strengthens considerably from 6 UTC
onwards, first at higher levels (z 10 km) and later in the mid-troposphere attaining values of 40 m/s at 10 km height and 20 m/s at 5 km at 18 UTC (right panel in Fig. 19a and 20a). Remarkably is the strong wind shear of 20 m/s/km between 8 and 9 km around noon on 7 Nov which is well captured in the background and analysis field (Fig. 20). Concatenated with the jet passage is a change in wind direction from slight southerly to blustery northerly wind. Also, the timing and the strength of the jet-stream passage is well captured by CNTRL and OPER’99 (not shown). Another interesting feature is the formation of the north fohn¨ associated with the passage of a cold front in the afternoon on 6 Nov. Documented by the UHF profiler, this north fohn¨ event lasts for 15 hours (winds larger than 15 m/s below 3 km height; Fig. 19b). High wind velocities exceeding 20 m/s prevail from 17 to 21 UTC and are ceasing thereafter. However, the VHF profiler at Lonate which lowest measuring level is at 1.7 km height misses this low-level wind maximum completely (Fig. 19a). This has consequences for the MAP-RA. While the short-range model forecast captures the formation of the north fohn¨ (background field in Fig. 20b), the vertical extent and the strength of the low-level wind are diminished in the analysis (Fig. 20a). Here the contradicting information of both Lonate windprofilers had to be compromised resulting in the decrease of the north fohn¨ in MAP-RA. Exclusion of the windprofiler data retains the strong north fohn¨ in the CNTRL analysis (not shown).
Technical Memorandum No. 401 21 MAP Re-Analysis
14 14 m/s m/s
12 12 40 40
35 35 10 10 30 30 8 8 25 25
6 6 20 20 Height [km] Height [km]
15 15 4 4
10 10 2 2 5 5
0 0 1 1 a 0 3 6 9 12 15 18 21 240 3 6 9 12 15 18 21 24 time time 6 Nov 99 7 Nov 99
14 14 m/s m/s
12 12 40 40
35 35 10 10 30 30 8 8 25 25
6 6 20 20 Height [km] Height [km]
15 15 4 4
10 10 2 2 5 5
0 0 1 1 b 0 3 6 9 12 15 18 21 240 3 6 9 12 15 18 21 24 time time
Figure 20: Time-height diagramme of the horizontal wind velocity of the MAP-RA during IOP15 at Lonate: (a) analysis and (b) background. The arrows indicate the direction of the horizontal wind.
The impact of the VHF profiler on the analysis is also visible in the location of the shear zone, where a southerly flow at lower levels alternates to a northerly flow aloft (in 9 km altitude from 6 Nov 12 UTC onwards; Fig. 20a). The windprofiler observations (Fig. 19a) compare better with the analysis than with the background of MAP- RA (Fig. 20), i.e. the strength of the southerly winds is increased (by about 5 m/s) and its location is lifted to about 8 km height in the analysis.
7 Validation using GPS based humidity measurements
In atmospheric processes, humidity is a highly variable parameter that plays a crucial role in atmospheric motions on a wide range of scales in space and time. Limitations in humidity observation accuracy, both its temporal and spatial coverage, lead to problems in predicting clouds and precipitation (e.g. IOP2a, section 6.2). Due to these limitations, the evaluation of humidity is also difficult. The emerging ground-based Global Posi- tioning System (GPS) network gives the opportunity to validate the vertical integral of model humidity fields. Estimation of the integrated water vapor (IWV) in the atmosphere from the anomalous delays in the radio signal transmitted by the GPS satellites has an accuracy that is comparable to that of radiosondes (Bock et al., 2003) which are, at present, the primary data source of humidity vertical profiles. The data has been retrieved from the MAGIC website1. In conjunction with software made available through
the COST716 action, the measured Zenith Total Delay (ZTD) has been converted into IWV values at available
1 ¡ ¢ htt p : www¢ acri f r magic
22 Technical Memorandum No. 401 MAP Re-Analysis
Torino Venezia
a b 17. 18. 19. 20. 21.Sep 17. 18. 19. 20. 21.Sep
Genova Medicina
c d 17. 18. 19. 20. 21.Sep 17. 18. 19. 20. 21.Sep
Figure 21: Time-series of GPS derived integrated water vapour observations (black) and analyzed counterparts of MAP- RA (red) and OPER’99 (blue) for (a) Torino, (b) Venezia, (c) Genova and (d) Medicina for the 5-day period starting on 17 Sep 99. The analysis departures (o-a) are depicted in dashed lines.
Alpine GPS stations. Time series of observed and analyzed IWV contents are presented for two 5-day periods (IOP2 and IOP8) at southern Alpine GPS stations (Figs. 21 and 22), together with an intercomparison of the spatial humidity distribution on 21 Oct during IOP8 (Fig. 23). During IOP2 (17 to 21 Sep 99) the IWV content derived from GPS measurements is showing significant varia- tions ranging from 20 to 50 kg m2 at the four Italian stations Torino, Genova, Medicina and Venezia (Fig. 21). The mean values are influenced by the station altitude and its proximity to the Mediterranean Sea. Highest IWV contents are recorded at all stations on 20 Sep, coinciding with the strong rainfall event of IOP2b (see section 6.3). These IWV maxima are succeeded by a sharp decrease by nearly 20 kg m2, progressing eastward from Torino (at 9 UTC; Fig. 21a) and Genova (at 12 UTC; Fig. 21c) to Medicina and Venezia at the Adriatic Coast (at 18 UTC; Fig. 21b,d). Generally, the analyses are drier than the GPS observations with a mean deviation ranging from 1.6 to 8.5 kg m2 (for different stations and/or analysis). Whereas the main temporal evolution of the IWV content peaking during IOP2b is captured by the analyses, locally there are considerable differences between observations and analysis. One reason is the difference between station altitude and corresponding model height, e.g. the GPS station at Torino (eastern slopes of the Sea-Alps) has an altitude of 262 m above sea level, while in the MAP- suite this location is at 379 m and in OPER’99 even at 999 m altitude, resulting in a large systematic deviation between GPS observation and OPER’99 (Fig. 21a). Apart from this altitude effect, the analyses show the largest
Technical Memorandum No. 401 23 MAP Re-Analysis
Torino Venezia
19. 20. 21. 22. 23.Oct 19. 20. 21. 22. 23.Oct a b
Genova Medicina
19. 20. 21. 22. 23.Oct 19. 20. 21. 22. 23.Oct c d
Milano
Figure 22: Time-series of GPS derived integrated water vapour observations (black) and analyzed counterparts of MAP-RA (red) and OPER’99 (blue) for (a) Torino, (b) Venezia, (c) Genova, (d) Medic- 19. 20. 21. 22. 23.Oct ina and (e) Milano for the 5-day period starting on e 19 Oct 99. The analysis departures (o-a) are de- picted in dashed lines.
deviations for the site of Venezia, close to the Mediterranean Sea, yielding a mean IWV difference of 7.5 kg m2 (Fig. 21b). Here the proximity to the warm SSTs of the Mediterranean Sea seems to be the main reason for the differences, where small changes in the boundary layer wind field can cause large deviations in humidity advection. The highest single observation-minus-analysis errors occur on 17 Sep (IOP2a), with the differences exceeding 10 kg m2, likely due to the meso-scale processes involved in the generation of the squall-line which cannot be resolved by the IFS (see section 6.2). Comparing the IWV observation-minus-analysis differences at the Italian stations during the 5-day period, MAP-RA shows a slightly better agreement with observations than OPER’99. However, given a GPS observation error of about 2 kg m2 (Bock et al., 2003), the intercomparison highlights the difficulties encountered to capture a realistic IWV content. For IOP8 (19 to 23 Oct 99), the GPS derived IWV contents show large deviations for all stations south of the Alps (the Milano station, only available in Oct, is also added) within this 5-day period, with observed IWV changes of up to 200 % within 48 hours (Fig. 22). The two observed IWV maxima on 21 and 23 Oct are reproduced by both the MAP-RA and OPER’99. However, the analyses at the four ’MAGIC’ stations (Torino,
24 Technical Memorandum No. 401 MAP Re-Analysis
ECMWF Analysis VT:Thursday 21 October 1999 12UTC Surface: total column water vapour/Surf: geopotential height 6°E 8°E 10°E 12°E 14°E 16°E 36 34 32 30 48°N 48°N 28 26 24
46°N 46°N 22 20 18 16 44°N 44°N 14 12 a b 10 8 6°E 8°E 10°E 12°E 14°E 16°E
ECMWF Analysis VT:Thursday 21 October 1999 12UTC Surface: geopotential(orography) ECMWF Analysis VT:Thursday 21 October 1999 12UTC Surface: total column water vapour/Surf: geopotential height ECMWF Analysis VT:Thursday 21 October 1999 12UTC Surface: total column water vapour 6°E 8°E 10°E 12°E 14°E 16°E 6°E 8°E 10°E 12°E 14°E 16°E 36 36
34 34
32 32 30 30 48°N 48°N 48°N 48°N 28 28 26 26 24 24
46°N 46°N 22 46°N 46°N 22 20 20 18 18 16 44°N 44°N 14 44°N 44°N 16 12 14 c 10 d 12 8 10 6°E 8°E 10°E 12°E 14°E 16°E 6°E 8°E 10°E 12°E 14°E 16°E
Figure 23: Integrated water vapour [kg m2] valid at 12 UTC on 21 Oct (IOP8): (a) GPS based observation, (b) MAP-RA, (c) CNTRL and (d) OPER’99 (Fig. 23a by courtesy of O. Bock).
Genova, Medicina and Venezia) are again too dry. In contrast, the experimental GPS station at Milano shows predominantly negative observation-minus-analysis errors, i.e. the analyses are too wet. Averaged over the 5- day period, the mean error of OPER’99 is larger than of MAP-RA at Milano, 2.8 versus 1.7 kg m2, respectively. The comparison of the IWV spatial distribution on 21 Oct 12 UTC indicates the quality of the MAP-RA. High values of IWV prevailing throughout the Po valley (Fig. 23a) are realistically represented in MAP-RA (Fig. 23b), whereas the IWV maxima in CNTRL and particularly in OPER’99 are located at the Ligurian Coast (Fig. 23c,d). The reanalyzed IWV content matches qualitatively as well as quantitatively with GPS derived values, attaining the maxima of more than 32 kg m2 close to Venezia and minima across the central Alps with values of less than 12 kg m2.
8 Conclusions
The MAP Re-Analysis constitutes an unprecedented description of the state of the atmosphere for the ten- week period in autumn 1999, when the SOP took place in the European Alps. The MAP Re-Analysis has been performed by the global assimilation system 4D-Var at the resolution T511/159L60 which allows a better use of MAP observations over the Alpine region. For instance, 3 times more humidity observations from radiosondes and even 5 times more humidity measurements at surface stations have been assimilated in the Alpine region compared to the operational analysis in 1999.
Technical Memorandum No. 401 25 MAP Re-Analysis
The main objectives of the MAP Re-Analysis, outlined in the introduction, are being met:
A comprehensive set of analyses describing the state of the atmosphere for the 70-day period of MAP SOP in autumn 1999 has been produced.
A formatted archive of the additional MAP observations has been created using BUFR format and has been transfered to the MAP Data Centre (MDC).
These uniformly formatted MAP observations as well as analyzed fields have been archived at the easily accessible MDC to promote European and international research.
Validation and diagnostic studies have been performed to understand the impact of the great variety of asynoptic MAP observations and also special care has been devoted to the evaluation of different synoptic features during some Intensive Observation Periods.
Special attention has been paid to European windprofilers, a data source which was not used during the SOP in NWP and only recently has been assimilated at ECMWF. Four of the 16 European windprofilers reporting during the SOP had to be denied in MAP-RA due to their large STD of background departures. These excluded stations comprise two British (Camborne, Dunkeswell), one French (Clermont) and one Italian windprofiler (L’Aquila). The remaining windprofilers have been seen to slightly dry the troposphere in the southern Alpine region and to moisten southern Italy and the Adriatic Sea. The windprofiler data introduce changes in the divergent wind field which feeds back on the humidity analysis. A twin re-analysis experiment has been accomplished without the use of the MAP observations. When MAP observations are assimilated, the reanalysed fields show slightly moister conditions in the southern Alpine region, southern France and over the Adriatic Sea. The comparison between the time-series of observed daily precipitation, averaged over the Po catchment south of the Alps, and the forecast daily rainfall (based on the MAP-RA) highlights some aspects of the model skill. The timing and the averaged precipitation amounts agree remarkably well. Furthermore, investigations on IOP2a, IOP2b, IOP8 and IOP15 illustrate some specific aspects of the MAP-RA. Different precipitation patterns in the southern Alpine region, fingerprints of different flow regimes prevailing during IOP2b and IOP8, are well captured by the model. In particular, windprofilers reinforce features as shear zones and jet-stream winds. Also these observations are able to modify the divergent wind field that sometimes has an errorneous feed back in the analyzed humidity. In fact, for IOP2a the increased subsidence (due to Lonate windprofiler) led to a drier boundary layer and consequently inhibits the formation of convection. Finally, the comparison of GPS derived integrated water vapour contents and the analyzed counterparts shows that the analyses follow the general trend of inter-diurnal humidity variations at southern Alpine GPS stations for two heavy precipitation events (IOP2 and IOP8). Generally, the ECMWF analyses seem to be roughly 2- 7 kg m2 too dry compared with GPS derived IWV content and they have large spatial and temporal variations. This underestimation of humidity can be partly attributed to the model resolution which plays a crucial role in representing orography and land-sea contrast realistically, both having a strong impact on humidity fields. The dry bias of the Vaisala radiosondes might be another element of explanation. The timely production of the deliverables and their storage at the public accessible MAP Data Centre will hopefully trigger further research dealing with the MAP SOP and eventually improve our understanding and the prediction of mountain-related atmospheric phenomena.
26 Technical Memorandum No. 401 MAP Re-Analysis
A Observation errors
Over the years, corrections on observational errors have been derived by statistical evaluation of the perfor- mance of the observing system, as component of the assimilation system. The observational errors for wind components, height, temperature and relative humidity are defined at the standard pressure levels. Some RMS (Root Mean Square) values of observation errors relevant for the present investigation are summarized in Ta- ble A.1.
Table A.1: Overview of the RMS observation errors of observation types deployed during the MAP campaign. Observation type Measured Parameter 3 selected levels 1000 hPa 500 hPa 200 hPa TEMP & PILOT wind 2.3 m/s 3.0 m/s 3.5 m/s Windprofiler wind 2.3 m/s 3.0 m/s 3.5 m/s TEMP temperature 1.7 K 1.2 K 1.5 K TEMP height 4.3 m 8.4 m 13.2 m TEMP relative humidity 0.17 0.17 0.17 SYNOP relative humidity 0.13
B Quality assessment of European windprofilers
In order to be able to identify the European windprofilers reporting poor quality measurements, observation statistics has been individually calculated. Generally, the amount and the quality of data is varying significantly among different windprofilers (Fig. B.1). While the Aberystwyth windprofiler is the most active, reporting data from the boundary layer (850 hPa) up to the lower stratosphere (50 hPa), other windprofilers measure only the lower and mid-troposphere (1000 to 700 hPa; e.g. Vienna, Bad Ragaz). Good quality data are reported from the three windprofilers at Aberystwyth (UK), La Ferte Vidame (F) and Lindenberg (D), for which the bias of
the background departure is less than 2 m s in the entire troposphere. However, the bias of some profilers is exceeding values of 4 m s (3.5 m s is the observation error in the upper troposphere). Consequently, it has been decided to exclude four of the 16 European windprofilers in the MAP-RA: Camborne (UK), Dunkeswell (UK), Clermont (F) and L’Aquila (I) (Fig. B.1 b,c,h and m).
Technical Memorandum No. 401 27 e9cp MAP SOP 1999090700-1999092100(6) background departure o-b EUprofiler-Uwind areaNSEW= 53/ 52/ -4/ -5 analysis departure o-a used U STD.DEV BIAS 5 0 5 10 0 10 20 0 20 e9cp 30MAP SOP 1999090700-1999092100(6) 0 background departure 30 o-b EUprofiler-Uwind 50 areaNSEW= 51/ 50/ -5/ 50 -6 50 70 1346 analysis departure 70 o-a used 100 U 3957 100 150 4633 150 200 STD.DEV 3916 BIAS 200 250 5 5199 0 250 5 300 10 6435 0 300 10 400 20 6911 0 400 20 e9cp 500 30MAP SOP 1999090700-1999092100(6) 8688 0 background departure 500 30 o-b EUprofiler-Uwind 700 50 areaNSEW= 51/ 50/ 8046 -3/ 0 -4 700 50 850 70 1976 0 analysis departure 850 70 o-a used1000 100 U 0 1000 100 150 0 2 4 6 8 0 -6 -4 -2 0 2 4 6 150 200 STD.DEV 0 BIAS 200 250 5 0 250 5 e9cp 300 10MAP SOP 1999090700-1999092100(6) 149 0 background departure 300 10 o-b 400 20 1706 0 400 20 EUprofiler-VwindEUprofiler-Uwind 30 areaNSEW= 53/52/ 52/51/ -4/ 5/ 0 -5 4 30 500 2766 analysis departure 500 o-a used 700 50VU 3568 0 700 50 850 70 3765 0 850 70 1000 100 STD.DEV 560 0 BIAS 1000 100 150 5 0 2 4 6 8 0 -6 -4 -2 0 2 4 6 150 5 200 10 0 20010 250 20 0 25020 e9cp 300 30MAP SOP 1999090700-1999092100(6) 204 0 background departure 30030 o-b EUprofiler-VwindEUprofiler-Uwind 400 50 areaNSEW= 51/47/ 50/46/ -5/ 10/839 50 0 -6 9 40050 500 70 12351346 0 analysis departure 50070 o-a used 100700 VU 14943959 0 100700 150850 15124634 0 150850 1000 200 3985 83 0 1000200 0 STD.DEV2 4 6 8 -6 -4 -2BIAS0 2 4 6 250 5 5318 0 250 5 300 10 6580 0 300 10 400 20 0 400 20 e9cp 400 MAP SOP 1999090700-1999092100(6) 7347 background departure 400 o-b e9cp 500 30MAP SOP 1999090700-1999092100(6) 8536 408 0 background departure 500 30 o-b EUprofiler-VwindEUprofiler-Uwind 700 50 areaNSEW= 51/49/ 50/48/ 82332061 -3/ 1/ 0 -4 0 700 50 850 70 20632302 0 analysis departure 850 70 o-a used1000 100 VU 333 0 1000 100 150 0 2 4 6 8 0 -6 -4 -2 0 2 4 6 150 200 STD.DEV 0 BIAS 200 250 5 0 250 5 10 0 10 e9cp 300 MAP10MAP SOP SOP 1999090700-1999092100(6) 1999090700-1999092100(6) 264149 0 background departure 300 10 o-b 400 20 1729 908 0 400 20 EuropeanEUprofiler-Vwind 500 30 wind profilerspeed areaNSEW= areaNSEW= 52/ 51/ 53/ 3110 5/ 052/ 4 -4/ -5 500 30 500 2755 analysis departure 500 o-a used 700 U50V 3667 303 0 700 50 850 70 3887 824 0 850 70 1000 100 STD.DEV 1032 560 0BIAS OFBIAS SPEEDMAP Re-Analysis1000 100 150 5 0 2 4 6 8 951 0 -6 -4 -2 0 2 4 6 150 5 200 10 737 0 20010 250 20 STD. DEV 516 0 BIAS 25020 e9cp 300 MAP30MAP SOP SOP 1999090700-1999092100(6) 1999090700-1999092100(6) 194720 0 background departure 30030 o-b a 400 50 811773 50 0 40050 EuropeanEUprofiler-Vwind 500 wind profilerspeed areaNSEW= areaNSEW= 47/ 46/ 51/ 10/ 965 50/ 9 -5/ -6 500 500 70 12251346 0 analysis departure 50070 o-a used 100700 UV 15573957 873 0 100700 150850 15484633 160 0 150850 1000 200 83 0 1000200 200 0 STD.DEV2 4 6 8 3916BIAS-6 -4 -2OFBIAS0 SPEED2 4 6 200 250 5 5199 0 250 5 300 10 6435 0 300 10 400 20 0 400 20 e9cp 400 MAP SOP 1999090700-1999092100(6) 6911 background departure 400 o-b e9cp 500 MAP30 SOP 1999090700-1999092100(6) 8688 382 0 background departure 500 30 o-b EuropeanEUprofiler-Vwind 700 50 wind profilerspeed areaNSEW= areaNSEW= 49/ 48/ 51/ 80462032 1/ 50/0 0 -3/ -4 700 50 850 70 19762376 0 analysis departure 850 70 o-a used1000 100 UV 338 0 1000 100 150 0 2 4 6 8 0 -6 -4 -2 0 2 4 6 150 200 STD.DEV 0BIAS OFBIAS SPEED 200 250 5 0 250 5 e9cpb 300 MAP10 SOP 1999090700-1999092100(6) 237149 0 background departure 300 10 o-b 400 20 1706 874 0 400 20 European 30 wind profilerspeed areaNSEW= 52/ 051/ 5/ 4 30 500 31302766 analysis departure 500 o-a used 700 U50 3568 303 0 700 50 850 70 3765 827 0 850 70 1000 100 STD.DEV 1033 560 0BIAS OF SPEED1000 100 150 5 0 2 4 6 8 958 0 -6 -4 -2 0 2 4 6 150 5 200 10 729 0 20010 250 20 521 0 25020 e9cpc 300 MAP30 SOP 1999090700-1999092100(6) 204723 0 background departure 30030 o-b 400 839778 400 European 50 wind profilerspeed areaNSEW= 47/ 46/ 0 10/ 9 50 500 70 1235 968 0 analysis departure 50070 o-a used 100700 U 1494 906 0 100700 150850 1512 164 0 150850 1000 83 0 1000 200 0 STD.DEV2 4 6 8 0BIAS-6 -4 -2OF0 SPEED2 4 6 200 250 5 0 2 4 6 8 0 -6 -4 -2 0 2 4 6 250 5 300 10 0 300 10 400 20 0 400 20 e9cpd 500 MAP30 SOP 1999090700-1999092100(6) 408 0 background departure 500 30 o-b European 700 50 wind profilerspeed areaNSEW= 49/ 2061 48/0 1/ 0 700 50 850 70 2302 0 analysis departure 850 70 o-a used1000 100 U 333 0 1000 100 150 0 2 4 6 8 0 -6 -4 -2 0 2 4 6 150 200 STD.DEV 0BIAS OF SPEED 200 250 5 0 250 5 e 300 10 264 0 300 10 400 20 908 0 400 20 500 30 3110 0 500 30 700 50 303 0 700 50 850 70 824 0 850 70 1000 100 1032 0 1000 100 150 0 2 4 6 8 951 -6 -4 -2 0 2 4 6 150 200 737 200 250 516 250 f 300 720 300 400 773 400 500 965 500 700 873 700 850 160 850 1000 0 1000 0 2 4 6 8 -6 -4 -2 0 2 4 6
Figure B.1: Standard deviation (left) and bias (right) of the background (o-b; solid line) and analysis (o-a; dotted) departures of the European windprofilers of ALDAT from 7 Sept till 21 Sept 99 : (a) Aberystwyth, (b) Camborne, (c) Dunkeswell, (d) Cabauw, (e) Julier Pass, (f) La Ferte Vidame.
28 Technical Memorandum No. 401 e9cp MAP SOP 1999090700-1999092100(6) background departure o-b EUprofiler-Uwind areaNSEW= 46/ 45/ 9/ 8 analysis departure o-a used U STD.DEV BIAS 5 0 5 10 0 10 20 0 20 30 0 30 50 0 50 70 0 70 e9cp 100 MAP SOP 1999090700-1999092100(6) 595 background departure 100 o-b 150 1069 150 EUprofiler-Uwind 200 areaNSEW= 46/ 45/ 6884/ 3 200 250 587 analysis departure 250 o-a used 300 U 862 300 400 STD.DEV 980 BIAS 400 500 5 2283 0 500 5 700 10 2626 0 70010 850 20 1330 0 85020 1000 30 114 0 1000 30 50 0 2 4 6 8 0 -6 -4 -2 0 2 4 6 50 70 0 70 e9cp 100 MAP SOP 1999090700-1999092100(6) 404 background departure 100 o-b e9cp 150 MAP SOP 1999090700-1999092100(6) 1205 background departure 150 o-b EUprofiler-VwindEUprofiler-Uwind 200 areaNSEW= 46/53/ 45/52/ 15/8289/ 814 200 250 782 analysis departure 250 o-a used 300 VU 1137 300 400 1378 400 500 STD.DEV 1749 BIAS 500 e9cp MAP5 SOP 1999090700-1999092100(6) 0 background departure 5 o-b 700 10 1248 0 70010 850 10 10 850 10 EUprofiler-Uwind 20 areaNSEW= 48/ 47/ 10/ 0 9 20 1000 30 0 analysis departure 1000 30 o-a used 30U 0 2 4 6 8 0 -6 -4 -2 0 2 4 6 30 50 0 50 70 0 70 e9cp 100 MAP SOPSTD.DEV 1999090700-1999092100(6) 227 BIASbackground departure 100 o-b e9cp 100 MAP5 SOP 1999090700-1999092100(6) 594 0 background departure 100 5 o-b EUprofiler-Uwind 150 10 areaNSEW= 49/ 48/ 1056 17/839 0 16 15010 EUprofiler-Vwind 200 20 areaNSEW= 46/ 45/ 1311 6634/ 0 3 analysis departure 20020 o-a 250 617 analysis departure 250 o-a used 250 30VU 568 0 25030 300 50 1327 822 0 30050 400 70 STD.DEV 1641 951 0 BIAS 40070 500 5 STD.DEV 2235 0 BIAS 500 5 e9cp 100500 MAP5 SOP 1999090700-1999092100(6) 2329 0 background departure 100500 5 o-b 150700 10 2031 2616 0 150700 10 EUprofiler-Uwind 850 2010 areaNSEW= 48/ 47/ 4446 12/ 0 11 8502010 200850 20 1372 0 analysis departure 20085020 o-a 1000 250 30 1891 119 0 1000250 30 used 5030U 0 2 4 6 8 0 -6 -4 -2 0 2 4 6 5030 300 50 0 2 4 6 8 0 -6 -4 -2 0 2 4 6 30050 400 70 0 400 70 100 70 STD.DEV 0 BIAS 100 70 e9cp 100500 MAP5 SOP 1999090700-1999092100(6) 401176 0 background departure 100 500 5 o-b 700150 10 4392 0 background departure 70015010 o-b e9cp 200150 MAPMAP SOP SOP 1999090700-1999092100(6) 1999090700-1999092100(6) 1193 0 background departure 200150 o-b EUprofiler-Vwind 200850 20 areaNSEW= 53/ 52/ 9743 15/ 816 0 14 200 85020 EUprofiler-UwindEuropean1000 250 30 wind profilerspeed areaNSEW= areaNSEW= 43/ 42/ 46/ 14/ 045/ 13 9/ 8 analysis departure 1000 25030 o-a 300250 782 0 analysis departure 300250 o-a used 300 U50V 0 2 4 6 8 1110 0 -6 -4 -2 0 analysis2 4 departure6 300 50 o-a used 400 70U 0 40070 500400 1363 0 500400 500100 STD.DEV 1677 0BIAS OFBIAS SPEED 500100 e9cp 150700 MAP5 SOPSTD.DEV 1999090700-1999092100(6) 4020 0 BIASbackground departure 150 700 5 o-b MAP Re-Analysis 850700 10 5 7196 1231 0 850 70010 5 850200 10 01 85020010 EUprofiler-Vwind1000 250 20 areaNSEW= 48/ 47/ 1996 10/ 0 9 1000250 20 1000 3020 0 2 4 6 8 0 -6 -4 -2 0 analysis2 4 departure6 1000 3020 o-a 300 30 0 300 30 used 5030V 0 2 4 6 8 0 -6 -4 -2 0 2 4 6 5030 400 50 STD. DEV 0 BIAS 40050 500 70 847 0 500 70 e9cp 100 70MAP SOPSTD.DEV 1999090700-1999092100(6) 227 0 BIASbackground departure 100 70 o-b e9cpg 100700 MAP 5 SOP 1999090700-1999092100(6) 7060 595 0 background departure 100700 5 o-b 850150 10 26551069 850 0 850 15010 EUprofiler-Vwind 200150 areaNSEW= 49/ 48/ 1281 17/ 0 16 200150 European1000 200 20 wind profilerspeed areaNSEW= 46/ 688 45/0 4/ 3 analysis departure 1000200 20 o-a used 250 30V 0 2 4 6 8 615 587 0 -6 -4 -2 0 analysis2 4 departure6 25030 o-a used 300250 U 1323 0 300250 300 50 862 0 300 50 400 70 STD.DEV 1642 980 0 BIAS 40070 500400 5 STD.DEV 2227 238 0BIAS OF SPEED 500400 5 e9cp 500100 MAP5 SOP 1999090700-1999092100(6) 2283 724 0 background departure 500 100 5 o-b 150700 10 2020 2626 0 150700 10 EUprofiler-Vwind 850700 2010 areaNSEW= 48/ 47/ 4554 12/ 734 0 11 8507002010 850200 20 1330 0 analysis departure 850 20020 o-a 1000 250 30 1884 114 0 1000250 30 used1000 5030V 0 2 4 6 8 0 -6 -4 -2 0 2 4 6 1000 5030 300 50 0 2 4 6 8 0 -6 -4 -2 0 2 4 6 30050 400 70 0 400 70 100 70 STD.DEV 0 BIAS 100 70 100500 5 404194 0 background departure 100 500 5 o-b e9cph 700150 MAP10 SOP 1999090700-1999092100(6) 4473 0 70015010 e9cp 200150 MAP SOP 1999090700-1999092100(6) 1205 0 background departure 200150 o-b 850 20 9736 0 85020 European 250200 wind profilerspeed areaNSEW= 53/ 828 52/ 0 15/ 14 250200 EUprofiler-Vwind1000 250 30 areaNSEW= 43/ 42/ 14/ 782 0 13 analysis departure 1000250 30 o-a used 300 U50 0 2 4 6 8 0 -6 -4 -2 0 analysis2 4 departure6 30050 o-a used 400300 V 1137 0 400300 400 70 1378 0 400 70 100500 STD.DEV 0BIAS OF SPEED 100500 700500 5 STD.DEV 4022 1749 0 BIAS 700 500 5 e9cp 700150 MAP 5 SOP 1999090700-1999092100(6) 1248 0 background departure 700150 5 o-b 200850 10 7098 0 200850 10 European1000 850 2010 wind profilerspeed areaNSEW= 48/ 1965 47/ 01 10/ 9 1000 8502010 1000 250 20 0 2 4 6 8 0 -6 -4 -2 0 analysis2 4 departure6 1000 25020 o-a 300 30 0 300 30 used U5030 0 2 4 6 8 0 -6 -4 -2 0 2 4 6 5030 400 50 0 40050 500 70 909 0 500 70 100 70 STD.DEV 227 0BIAS OF SPEEDbackground departure 100 70 o-b e9cpi 100700 MAP 5 SOP 1999090700-1999092100(6) 7120 0 100700 5 850150 10 2481 839 0 850 15010 European 200150 wind profilerspeed areaNSEW= 49/ 1311 48/ 0 17/ 16 200150 1000 200 20 0 analysis departure 1000200 20 o-a used 250 U30 0 2 4 6 8 617 0 -6 -4 -2 0 2 4 6 25030 300250 1327 0 300250 300 50 0 300 50 400 70 STD.DEV 1641 0BIAS OF SPEED 40070 500400 5 2235 215 0 500400 5 e9cp 500100 MAP SOP 1999090700-1999092100(6) 719 0 background departure 500100 o-b 150700 10 2031 0 150 70010 European 850700 20 wind profilerspeed areaNSEW= 48/ 4446 721 47/ 0 12/ 11 85070020 850200 0 analysis departure 850200 o-a 1000 250 30 1891 0 1000250 30 used1000 U50 0 2 4 6 8 0 -6 -4 -2 0 2 4 6 1000 50 300 0 2 4 6 8 0 -6 -4 -2 0 2 4 6 300 400 70 STD.DEV 0BIAS OF SPEED 400 70 500100 5 176 0 500100 5 j 150 0 150 700 10 4392 0 background departure 70010 o-b e9cp 850200 MAP20 SOP 1999090700-1999092100(6) 9743 0 85020020 European1000 250 30 wind profilerspeed areaNSEW= 43/ 42/ 0 14/ 13 1000 25030 300 50 0 2 4 6 8 0 -6 -4 -2 0 analysis2 4 departure6 30050 o-a used 400 U70 0 40070 100500 0 100500 k 700 STD.DEV 4020BIAS OF SPEED 700 150 5 0 150 5 200850 7196 0 200850 1000 10 1996 0 1000 10 250 20 0 2 4 6 8 0 -6 -4 -2 0 2 4 6 25020 300 30 0 30030 400 50 0 40050 l 500 70 847 0 50070 100700 7060 0 100700 150850 2655 0 150850 1000 200 0 1000200 250 0 2 4 6 8 0 -6 -4 -2 0 2 4 6 250 300 0 300 400 238 400 m 500 724 500 700 734 700 850 0 850 1000 0 1000 0 2 4 6 8 -6 -4 -2 0 2 4 6
Figure B.1(cont’d): Standard deviation (left) and bias (right) of the background (o-b; solid line) and analysis (o-a; dotted) departures of the European windprofilers: (g) Lonate, (h) Clermont, (i) Lindenberg, (j) Bad Ragaz, (k) Vienna, (l) Innsbruck and (m) L’Aquila.
Technical Memorandum No. 401 29 MAP Re-Analysis
C Products
C.1 MAP observations
The supplementary observations available for the MAP Re-Analysis project comprise data recorded by roughly 11.000 additional surface stations, an enhanced radiosonde network with some stations reporting every three hours, more than a dozen European windprofilers as well as flight data from 8 research aircrafts from which dropsondes were also released. After the acquisition of these observations from the MAP Data Centre (MDC) in Zurich,¨ the data has been formatted in BUFR code (Tab. C.2). Subsequently, all the observations were archived in 6-hourly intervals and were transfered back to the MDC.
Table C.2: List of MAP observations which have been formatted in BUFR code. Observing System BUFR observation type subtype Surface stations 0 1 European windprofiler 2 96 Radiosondes 2 101 Dropsondes 2 103 Aircraft data 4 144
C.2 Analyzed fields
The basic analyzed variables include not only the conventional meteorological wind, temperature and humidity fields, but also model products currently available in the ERA-40 Re-Analysis. The parameters of the MAP Re-Analysis have been archived with a horizontal resolution of T511 for upper air fields, and a reduced Gaussian Grid with approximately uniform 40 km spacing for surface und other grid-point fields. Upper air data (see Table C.3) have been saved at each of the 60 ”full” model levels and at 23 pressure
levels. Additionally, a subset of upper air parameters were archived on fifteen isentropic surfaces as well as on the PV 2 surface (for details see ERA-40 Archive Plan, 2000). Surface and single level parameters produced by the analysis are given in Table C.6. Extra fields from the physical parameterisations accumulated over three hour intervals are post-processed for 12 hour forecasts both at 00 and 12 UTC. The parameters used to validate clear sky radiation, to support trajectory studies and to investigate the net tendencies from parameterised processes, are listed in Tables C.4 and C.5.
30 Technical Memorandum No. 401 MAP Re-Analysis
Table C.3: Upper air parameters on model (ml) and pres- Table C.6: Surface and single level parameters analyzed sure levels (pl) analyzed three hourly. three hourly (as type 4V). Parameter ml pl Code Units Parameter Code Units 2 2
surface geopotential x 129 m s sea surface temperature 34 K
2 2 ¡
¢ £ geopotential x 129 m s sea ice fraction 31 0 1 2 2 temperature x x 130 K surface geopotential 129 m s
1 2
specific humidity x x 133 kg kg total column water 136 kg m
1 2
vertical velocity x x 135 Pa s total column water vapour 137 kg m 1
vorticity x x 138 s soil temperature level 1 139 K log surface pressure x 152 Pa soil temperature level 2 170 K 1
divergence x x 155 s soil temperature level 3 183 K relative humidity x 157 % soil temperature level 4 236 K
1 3 3
cloud liquid water cont. x 246 kg kg soil moisture (4 levels) 39-42 m m 1
cloud ice water cont. x 247 kg kg Charnock parameter 148
¡ £ cloud cover x 248 0 ¢ 1 mean sea level pressure 151 Pa stand. deviation orography 160 m anisotropy of orography 161 m angle of subgrid-scale oro. 162 m
slope of subgrid-scale oro. 163 m
¡ £ total cloud cover 164 0 ¢ 1 1
10 m eastward wind comp. 165 ms 1
10 m northward wind comp. 166 ms 2 metre temperature 167 K Table C.4: Extra fields accumulated from the physical 2 metre dewpoint 168 K 2
parameterisations archived at full model levels. downw. surface solar rad.(acc) 169 W m s
¡ £ Parameter Code Units land/sea mask 172 0 ¤ 1 surface roughness 173 m Short wave radiative tendency 100 K albedo (climate) 174 Long wave radiative tendency 101 K 2 downw. surface thermal rad. (acc) 175 W m s
Clear sky short wave rad. tendency 102 K ¡ £ low cloud cover 186 0 ¢ 1
Clear sky long wave rad. tendency 103 K ¡ £
1 medium cloud cover 187 0 ¢ 1 ¡
u tendency 112 ms £ 1 high cloud cover 188 0 ¢ 1 v tendency 113 ms latitudinal component of
T tendency 110 K 2
gravity wave stress (accum.) 195 Nm s q tendency 111 kg kg meridional component of 2
gravity wave stress (accum.) 196 Nm s 2
gravity wave dissipation 197 W m s skin reservoir content 198 m of water runoff (accum.) 205 m of water log. surface roughness length (m) for heat 234
skin temperature 235 K
¡ £ low vegetation cover 27 0 ¢ 1
Table C.5: Extra fields accumulated from the physical ¡ £ parameterisations saved at half model levels. high vegetation cover 28 0 ¢ 1 low vegetation type 29 index Parameter Code Units high vegetation type 30 index 2 Updraught mass flux 104 kg m snow temperature 238 K 2 Downdraught mass flux 105 kg m snow albedo 32 2 Updraught detrainment rate 106 kg m snow density 33 2 Downdraught detrainment rate 107 kg m snow evaporation (accum.) 44 m 2 Total precipitation profile 108 kg m snow melt (accum.) 45 m Turbulent diff. coefficient for heat 109 m2 sea ice temperature (4 layers) 35-38 K
Technical Memorandum No. 401 31 MAP Re-Analysis
D MARS retrieval template
The following template is retrieving 3-hourly model level fields of main meteorological parameters T, W, U, V, LNSP, Z and Q for an European area on 17 Sept 1999:
retrieve
type 4v
class rd
expver e9mi
stream oper
¡
AREA 80 60 20 80
GRID 256
DATE 19990917
T IME 03 15
STEP 0 3 6 9
PARAM T W U V LNSP Z
LEVELLIST 1 to 60
LEVTYPE ML
REPRES SH
¢ ¢
target ml 4v
retrieve
PARAM Q
REPRES GG
¢ ¢
target ml 4v
Note on type 4v and an: The final 4D-Var trajectory is post-processed every 3 hours. Fields called 4v are created with initial date and time the start of the window (03UTC or 15UTC) and steps every 3 hours. The 4v field valid at 12 UTC or 00 UTC, is then renamed as the final analysis (type=an) for the atmospheric fields. The cycling from one cycle to the next is performed by taking these analysis fields, together with the surface fields updated by the SST, snow and soil moisture analyses as input to a 12-hour forecast which produces the background for the next cycle.
32 Technical Memorandum No. 401 MAP Re-Analysis
Acknowledgments
The MAP Re-Analysis Project was founded by the Mesoscale Alpine Programme of National Weather Services. Thanks to many colleagues at the Centre for helping to perform the Re-Analysis Project at ECMWF. We are grateful to Jan Haseler for the technical support creating the MAP-suite and Milan Dragosavac for BUFRizing the MAP observations. The authors thank Maria Tomassini (DWD) for providing software to convert GPS measured ZTD in IWV. Olivier Bock (CNRS) kindly provided IWV values of the Milano GPS site. Erik Andersson and Anton Beljaars helped to sharpen the line of argument.
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Technical Memorandum No. 401 33 MAP Re-Analysis
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34 Technical Memorandum No. 401