taupinhas/accepted/2018/alpert_ru… · web viewinternational journal of climatology, 29(15),...

Confidential manuscript submitted to GRL

First Daily Mapping of Surface Moisture from Cellular Network Data and Comparison with Both Observations/ECMWF Product

P. Alpert1, and Y. Rubin1

1Porter School of the Environment and Earth Sciences, Tel Aviv University, Tel Aviv, Israel.

Corresponding author: Yoav Rubin ([email protected])

† Pinhas Alpert ([email protected])

Key Points:

First daily mapping of surface moisture from cellular data

High-resolution humidity fields obtained from cellular data will improve future forecasting

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Abstract

Air-moisture maps were generated based on Commercial Microwave Links (CML) data over N. Israel and compared for the first time to ECMWF ERA-Interim maps for Apr 2017. Several skill scores of the different maps were calculated against the observed humidity from 39 weather stations. High correlations range, 0.75-0.9, were found between the new links' humidity fields and the stations. The numbers of best- correlated links are found to be higher compared to ECMWF, i.e., 31 stations vs. 8. Also, in the measure of standard deviation (STD) the links performed better, 38 vs. 1. However, for the mean humidity, the ECMWF is doing better, i.e. 14/25 for links/ECMWF. The links' humidity thus provides a more accurate picture of the observed moisture as compared to current weather prediction product. Consequently, the high-resolution humidity fields obtained from the CML seem to have the potential to improve future forecasting of precipitation.

Plain Language Summary

Atmospheric humidity has a cardinal part in weather prediction. Weather forecasting heavily relies on atmospheric models, the accuracy of which is largely determined by the quality of the measurements used for its input. Current humidity measuring tools cannot effectively provide the ideal requirements for weather prediction purposes. The method presented here provides a unique way of obtaining this type of high-resolution measurements, based on data collected by cellular networks.

Since the humidity disturbs the signals between two antennas, it can be measured based on information on the signal.

In this research, cellular humidity maps were generated for the first time. The maps were compared to products from a well-known forecast model,the ECMWF (European Centre for Medium-Range Weather Forecasts).

The cellular humidity maps showed better correlations with weather stations, which represent the real humidity in the air. Good results were obtained also for other statistical skills, indicating the quality and the accuracy of the cellular measurements.

The humidity derived from the cellular network was found to be more accurate as compared to the forecast models. Consequently, the new humidity-measuring method is likely to improve in the future the weather prediction, with particular relevance to heavy rainfall.

1. Introduction

Atmospheric humidity has a cardinal part in a variety of environmental processes (e.g. Allan et al., 1999) and in numerical weather prediction (NWP) particularly in prediction of moisture and rainfall. The water vapor evaporation and condensation cycle plays a significant role in the Earth's energy budget by transferring heat from the surface to the atmosphere and vice-versa and having a large effect on extreme rainfall events (Jin et al., 2011, Krichak et al., 2016). Meteorological weather forecasting heavily relies on atmospheric model results, the accuracy of which is largely determined by the quality of its initial conditions. Humidity, in particular, has a crucial role in the model initialization. For instance, the Mesoscale Alpine

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Program (MAP) which was set out to improve prediction of heavy rainfall and flooding, concluded that accurate initialization of moisture fields was crucial for better predictions (Ducrocq et al., 2002). Current humidity fields are predominantly obtained by surface stations, radiosonde or from satellite remote-sensed data such as GPS humidity. Surface stations normally provide point observations, and therefore suffer from low spatial representation. Furthermore, moisture is a field with unusually high mesoscale variability as demonstrated by structure functions (Lilly and Gal-Chen, 1983). In addition, there is limited accessibility in heterogeneous terrain, or in general, in areas with complex topography to establish humidity gauges. Satellites do allow for a large area to be covered but are frequently not accurate enough with surface moisture. One satellite tool is employing GPS moisture. It provides the integrated humidity through the atmospheric columns (e.g., Larson et al., 2008). However, surface moisture is not well-known employing GPS moisture. While, here we suggest (see next section) a method for measuring the humidity near the surface level at ~30 m above the ground, which is more efficient for surface humidity estimation.

The near-surface moisture field is often the most important variable for convection predictions. Radiosonde, which is typically launched only 2-4 times a day, also provides very limited information due to low spatial and temporal resolution. Additionally, these monitoring methods are quite costly for implementation, deployment and maintenance. Because of different surface perturbations, a point measurement close to the surface (for example 2 m above ground as required for standard meteorological surface station) is often not satisfactory for model initialization. An ideal requirement for meteorological modeling purposes is an area-averaged measurement of near-surface moisture over a box with the scale of the model's grid and at an altitude of a few tens of meters which normally better fits the lowest model layer. Current measuring tools cannot effectively provide such data.

The method presented here, however, provides a unique way of obtaining precisely this type of measurement and is based on data collected by wireless communication networks (hereafter, CML, in brevity for Commercial Microwave Link). This type of data was shown first as accurate in rainfall monitoring (Messer et al., 2006, Leijnse et al., 2007a, Zinevich et al. 2008, 2009, 2010, Rayitsfeld et al., 2012). This was based on the aRb relation in the calculation of rain microwave attenuation (Olsen, 1978). The technique introduced here was originally proposed by David et al. (2009), in order to measure atmospheric surface humidity using CML data.

Here, the CML skill in retrieving surface moisture will be compared to current most advanced assimilation systems like the ECMWF over N. Israel.

2. Data, Algorithms and Interpolation Methods:

2.1 Data2.1.1 Research area and period:

The research was conducted over north Israel for April 2017, Fig. 1. This area is most diverse and characterized by different landscapes such as, sea, forests, low vegetation, agricultural fields and towns. The variety of landscapes impacts significantly the surface moisture field often leading to sharp moisture gradients to be discussed later.

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Figure 1: A zoom over the study area around N. Israel, (32.30-33.40 N, 34.80-36.00 E). The numbers plotted on the figure are for the 39 Weather stations employed here. At the top left is the larger area over the E. Mediterranean with indication of the location of the study area box over N. Israel. The background map is a google-map indicating parks (green), main roads (white) borders (dashed), etc. The area is characterized by a low mountain range in the south west part (Mount Carmel), high mountain range in the north center (Upper Galilee), hills in the south west and the center (Menashe Heights and Lower Galilee), plains to the west along the Mediterranean coast (Zvulun Valley, Western Galilee and Israeli Coastal Plain), valleys in the south center and the east (Jesreel Valley, Galilee Panhandle and Jordan River Valley), heights to the north east (Golan Heights), and a lake in the east (Sea of Galilee).

Table 1: The 39 IMS Weather Stations Over the Study Area, Their Coordinates [Deg], Elevation [m] and the Median for the Period of the Calibration [g/m3].

Number Name Latitude [Deg]

Longitude [Deg]

Elevation [m]

Median [g/m3]

1 KEFAR GILADI 33.24 35.56 365 7.992 DAFNA 33.23 35.63 135 8.983 KEFAR BLUM 33.17 35.61 75 8.594 MEROM GOLAN PICMAN 33.13 35.80 945 6.965 ROSH HANIQRA 33.08 35.11 10 9.446 ELON 33.07 35.22 300 8.537 AYYELET HASHAHAR 33.02 35.57 170 8.278 SHAVE ZIYYON 32.98 35.09 5 8.879 ZEFAT HAR KENAAN 32.98 35.51 936 6.73

10 HARASHIM 32.96 35.33 830 7.0511 AMMIAD 32.92 35.51 215 8.3912 GAMLA 32.91 35.75 405 7.8113 ESHHAR 32.88 35.30 370 8.1114 KEFAR NAHUM 32.88 35.58 -200 9.88

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15 BET ZAYDA 32.88 35.65 -200 9.6016 DEIR HANNA 32.86 35.37 280 8.4217 AFEQ 32.85 35.11 10 9.3418 LEV KINERET 32.82 35.61 -213 11.4219 AVNE ETAN 32.82 35.76 375 8.0620 HAIFA REFINERIES 32.80 35.05 5 9.3221 HAIFA TECHNION 32.77 35.02 245 8.7622 HAIFA UNIVERSITY 32.76 35.02 475 8.4823 NEWE YAAR 32.71 35.18 115 9.2924 TAVOR KADOORIE 32.71 35.41 145 8.4525 ZEMAH 32.70 35.58 -200 9.5226 YAVNEEL 32.70 35.51 0 8.6827 MASSADA 32.68 35.60 -200 9.5928 EN KARMEL 32.68 34.96 25 9.0029 MERHAVYA 32.60 35.31 60 9.2530 EN HASHOFET 32.60 35.10 265 8.8431 AFULA NIR HAEMEQ 32.60 35.28 60 9.4932 ZIKHRON YAAQOV 32.57 34.95 175 8.9133 GALED 32.56 35.07 180 8.8434 TEL YOSEF 32.55 35.39 -65 9.1235 MAALE GILBOA 32.48 35.42 495 8.4036 HADERA PORT 32.47 34.88 5 9.3237 EDEN FARM 32.47 35.49 -120 9.2638 SEDE ELIYYAHU 32.44 35.51 -185 9.5839 EN HAHORESH 32.39 34.94 15 8.84

2.1.2 Data Sources:

Cellular Data: The received signal level (RSL) between 2 cellular bases (link) was provided by the local cellular company (Cellcom) daily at 0000 UTC with 24 h interval. In our study area, there are 238 links with continuous data for the study period. The quantization error is defined as the minimal interval for the RSL measurements and it is 0.3 dB. The full number includes many double links which means two signals going in both ways. However, here we have taken only one of the signals for each link in order to simplify the methodology. In nearly all links the two signals for each link are almost identical. The data we are receiving from the cellular companies with the required quantization error of 0.3 dB over this region is for 0000 UTC. Therefore we can only compare the measurements at this time and we choose a month period for having enough data for comparison.

Weather stations: Data from 39 weather stations located in the research area was collected at 2 am local winter time (0000 UTC). Table 1 lists the stations, their coordinates [in Degrees], elevation [m] and the median specific humidity for the period of the calibration [g/m3]. The humidity observed in the weather stations is presented in relative humidity units (%) so in order to compare it to the link humidity, the data was converted to absolute humidity (g/m3). The data was taken from the Israeli Meteorological Service (IMS) website (https://ims.data.gov.il). According to the IMS website there are 80 Israeli stations in total over

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Israel. For comparison, it should be stated that there are about 500 links over Israel that fit our humidity study requirements, i.e., small quantization error (0.3 dB), ~22 GHz frequency (this frequency has a moisture resonance, see next Sec. 2.2) and 2-5 km link lengths. These requirements are explained in the next section. This large number of links as compared to small number of meteorological stations, emphasizes the high potential of the cellular network as a tool for providing complementary information for surface humidity fields.

Reanalysis: The re-analysis data by 0.125°x0.125° resolution for 0000 UTC from the European Centre for Medium-Range Weather Forecasts (ECMWF) has been provided from their website (http://apps.ecmwf.int/datasets/data/interim-full-daily/levtype=sfc). The data was converted employing 2-m dew point and dry temperatures in order to obtain the absolute humidity (g/m3) (Lawrence, 2005).

2.2 Algorithms, Humidity calculation:

The attenuation of a microwave signal due to water molecules is most significant at the resonance line of 22.235 GHz (David et al., 2009). The specific attenuation γ (dB km -1) due to dry air and water vapor is well studied and can be evaluated using the following equation (Van Vleck, 1947, Liebe, 1985; ITU-RP.676-6, 2005):

(1) γ=γ v+γ 0+~N oise

Where γv is the specific attenuation due to water vapor, γ0 is the specific attenuation due to dry air and ~N oise is all other signal perturbations created as a result of factors other than water vapor. The specific attenuation due to dry air is one magnitude smaller than the specific attenuation due to water vapor for microwave signals with frequencies around the resonance line. Therefore γv can be obtained from equation 2.

(2) γv=0.182 f N ' ' ( ρ , p , f , T )

Where N ' ' is the imaginary part of the complex refractivity measured in N units, a function of pressure p(hPa), the link's frequencyf (GHz), temperature T (° C ¿and the water vapor density ρ(g/m3).

Since every link has a different receive signal level (RSL) spectrum and characteristic, the zero level will also be different. For the zero-level evaluation, it is necessary to take into account data from weather stations as explained next. The data from the weather stations is only used one time as the basis for calibration. In principle, we do not need side information for CML humidity measurements. The reason for using weather stations’ data is that the CML data we are receiving in this area from the cellular company is limited to RSL only. For the total attenuation we need to get also the TSL (Transmit Signal level). In locations where the companies share this data, the total attenuation can be measured and thus we can use only data from CML. Next, the explanation for how the zero-level attenuation is computed, is described.

First, the median absolute humidity from a weather station calculated for a preceding two-weeks period was taken and converted into specific attenuation using equation (2).

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The estimated attenuation for the median humidity ( Am) is calculated for each link depending on its length (L) from equation (3):

(3) Am=γ m L

At standard atmospheric conditions, the attenuation for a path length of 1 km at 22 GHz for a relatively low humidity value such as 5.5 g/m3 is ~0.15 [dB km-1]. Thus, cellular data at resolution of 0.3 is good for humidity measurements for 2 km links and above. Since the humidity spatial variation can be at smaller scales than the links’ length, the maximum length chosen for the measurements is 5 km. In most conditions this is an appropriate length for spatial measurements.

Subtracting the estimated attenuation ( Am) from the median of the received signal level (RSLm) for the same period provides the zero-level reference for each link (RSL0).

(4) RSL0=RSLm−Am.

The absolute humidity, ρ , for each link for the following month is then computed from equation (2) employing the attenuation for each day, based on the zero-level reference estimation from equation (4).

After calculating the humidity for each link, the correlation between each link and the closest weather station was computed. In order to eliminate errors due to environmental disturbances or any problems with the signal transmission, some links with low correlation values have been removed. This is being investigated and justified in section 2.4.

2.3. Interpolation:

Humidity field maps are generated based on the Inverse Distance Weighted (IDW) interpolation method (Cressman, 1959), and visualized next in 2-D maps. This method interpolates the humidity observations from the links on a 0.010x0.010 grid. The humidity in each grid point is calculated based on the observations located in the area limited by the influence radius, R. The weight given to each observation is a function of the distance from the grid point and calculated from equation (5). When getting closer to the grid point the weight increases and vice versa up to the influence radius, where the weight is zero following Cressman formula for weights, i.e.,

(5) w= R2−r2

R2+r2

R is the influence radius and r is the distance between the observation point and the grid point.

In order to find the optimal influence radius, R, the following experiment was done. The correlation between the humidity from a weather station, and the humidity at a CML grid point at the same location, was examined as the influence radius varies (range 10-70 km). The results showed that the correlation stabilizes and does not change much as the influence radius exceeds

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40 km. A spatial correlation analysis in order to determine the optimal R was not chosen here because for such an analysis a denser net of weather stations is required due to of the complex topography of the study region.

2.4. Threshold for filtering

Sometimes the humidity from a link does not have realistic values and the reason may not be clear. It is likely due to a local water related land-cover disturbance (parks, forest, roads, towns etc.) which can influence the microwave signal (Leijnse, 2007b).

In order to overcome unclear errors, the disturbed links are filtered using the following method. After calculating the humidity for each link, the correlation between each link and the closest weather station was calculated. In order to eliminate errors due to the aforementioned disturbances, the links with a correlation below a chosen low threshold are removed and then the correlations between the links' humidity fields after the interpolation and the stations are calculated. This was performed for different thresholds and consequently at each iteration the number of links used for calculating the humidity field drops along with the increase of the threshold correlation. The highest correlations between the humidity field from the links and the stations' humidity were obtained when the threshold for filtering was 0.4 for the period examined in this study (Apr 2017, Table 2 & supporting information Fig. 2 & Table 1). Since the difference in skill between threshold of 0.4 and zero are minor, we will use only threshold zero in forthcoming results.

2.5. Skills of the proposed methodology:

Absolute humidity based on RSL from the commercial microwave links (CML) has been compared to the absolute humidity measured by the stations' gauges using four metrics as follows: Correlation (CORR), Root-Mean-Square Deviation (RMSD), MEAN and Standard Deviation (STD). For comparison, all the four metrics have been also calculated for the ECMWF reanalysis (i.e., ERA-Interim).A Graphic User Interface (GUI) has been developed to allow efficient investigation of errors and therefore allowing to easily improving the methodology.

3. Results3.1. Comparison of monthly moisture variabilities from Links,

ECMWF and station data:

Figure 2a-d show the daily specific humidity variations at 0000 UTC (0200 LST) during April 2017 for 4 IMS stations including link result, ECMWF Interim-reanalysis vs. the IMS data. These 4 stations are out from 39 described below and represent one "bad" result from the links (a, Kefar Blum station); one relatively "good" (b, Elon) and two with similar correlations by the links to those from the ECMWF. It should be stated that in most stations as described below the link results are better in reproducing the moisture than the ECMWF, see next sections. For instance, with a threshold filtering of zero in 31 stations, a subset of the 39 stations the link-derived humidity is better (Table 2, Fig. 1; supporting information).

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The large variations among different stations was found to be dependent on several factors and particularly the local topography and distance from the coast. The relatively "bad" result for Kfar-Blum station (Fig. 2a) located within a valley (Fig.1, station number 3), i.e. CORR of only 0.58 for the links-station vs. 0.73 for the ECMWF seems to be caused by nearby links that extend over high topography. This low CORR is also reflected in the other skill parameters, i.e., an RMSD of 2.51 vs. 1.16 [g/m3] as well as MEAN humidity of 6.7 vs. 8.3 [g/m3]. The opposite is true for the mountainous station Elon (Fig. 2b, station number 6), located at an altitude of 300 m as compared to 75 m for Kefar Blum, in which the Links vs. ECMWF CORR are 0.71 vs. 0.37. The 4th skill on the top of the figures is the humidity-STD which is better for all stations in Fig. 2. For instance, in Elon (Fig. 2b), it is 1.5 compared to 2.3 while in the ECMWF it is lower, i.e. 1.1.

In the other two stations (Figs. 2c, d; stations' numbers 28, 29) when compared to ECMWF, they are similar in the CORR but better with the other three skills. Summary of all four skills for all weather stations is given in Table 2.

Figure 2a-d: Humidity graphs, in [g/m3], from the IMS stations, the links and the ECMWF reanalysis for

April 2017 at 0000 UTC (0200 LST) at four IMS stations: Kefar Blum (a), Elon (b), En Karmel (c) and

Merhavya (d). In Kefar Blum (Fig. 2a) the IMS-Links CORR is lower than the IMS-ECMWF CORR, in Elon

(2b) the IMS-Links CORR is higher than the IMS-ECMWF CORR and in En Karmel (2c) and Merhavya (2d)

the IMS-Links CORR and the IMS-ECMWF CORR are close. Threshold for filtering "bad" links was zero,

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i.e. links getting negative CORR were eliminated- see explanation in text. The number in parentheses next to

the station name indicates the station's location shown in Fig. 1.

3.2. Humidity mapping from links

Figs. 3a-f show the derived humidity maps over N. Israel for 2 days at 0000 UTC; a dry day on the 12th Apr 2017 (Fig.3a-c) and a moister day on the 20th Apr 2017 (Fig. 3d-f). The three maps are link-derived (a,d), IMS stations (b,e) and ERA-Interim (c,f). In general, the link & station maps include more detail and are similar while the ECMWF map is too moist and main variability is from west to east. For instance, the link map (Fig. 3a) captures the increased moisture over the Jesreel Valley (for location, Fig. 1) which is also noticed in the station map (3b) but not in the ECMWF map (3c). It is interesting to note that the number of employed links (here, 196 out of 238) from one period to another is quite stable. In addition, the percentage number of removed links was quite similar. However, these are not the same links for each period.

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Figures 3a-f: Humidity maps for the links (3a,d, 196 links, 0 threshold for filtering), IMS (3b,e, 39 stations) and the ECMWF (3c,f, 0.1250x0.1250 resolution) for April 12th, 2017 (3a-c) and April 20th, 2017(3d-f). The locations of the links (3a,d) and the stations (3b,e) are indicated in blue.

On the moister day (Fig. 3d-f), again the links and stations mapping show clearly the moisture drop over the higher Galilee Mts. and the relatively high moisture over the Mediterranean coast toward the Carmel Mts. with a sharp decreasing gradient towards the northern coast; both features are missing in the ECMWF map (f).

3.3. Comparison of links-stations humidity correlations vs. ECMWF correlations:

Table 2 shows the correlations (CORR) between the humidity field calculated from 196 links (after filtering of 42 links that show negative correlation) and the 39 IMS stations'

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humidity. This in comparison with the CORR obtained from the ECMWF reanalysis. In addition, Table 2 shows the skills based on RMSD [g/m3], the mean humidity, MEAN [g/m3] and for the standard deviation humidity, STD [g/m3]. Values in bold indicate the best result. When counting the numbers of best results, it is found that the links are leading compared to ECMWF in the CORR (31 vs. 8), in STD (38 vs. 1) while in the MEAN the ECMWF is doing better (14 vs. 25). The links and the ECMWF are doing similarly with the RMSD (20 vs. 19). The better STD result (1.67 g/m3 for the links compared to observed 2.1 and 1.35 for the ECMWF) as well as the CORR for the links is obviously reflecting the higher variability obtained by the links as discussed earlier regarding the maps (Sec. 3.2). The better MEAN result achieved by ECMWF suggests that further study is required in future on the reference/median value employed in the links methodology or other issue related to the proposed retrieval method. However, the average MEAN differences are quite small (bottom of Table 2; MEAN observed moisture is 9.05 as compared to 8.25 for the links and 8.78 g/m3 for ECMWF). The main result is that the links moisture performs mostly better than the ECMWF in capturing the air moisture. An obvious result is that the increase of the threshold for filtering of "bad" links improves the links' results. As mentioned earlier the "bad" links are probably due to specific location of the links relative to the character of the nearby surfaces (parks, forest, roads, towns etc.).

Table 2Four Skills Comparison for the Three Data Sources for April 2017

CORR RMSD MEAN STDName Links

IMS

ECMWF

IMS

Links

IMS

ECMWF

IMSLinks ECMWF IMS

LinksECMWF IMS

KEFAR GILADI 0.58 0.61 1.92 1.84 8.43 7.68 6.88 1.30 2.16 1.53DAFNA 0.57 0.69 2.19 1.38 8.43 8.45 6.97 1.30 1.93 1.57KEFAR BLUM 0.59 0.74 2.46 1.16 8.34 8.81 6.78 1.29 1.59 1.51MEROM GOLAN

PICMAN

0.69 0.68 1.59 1.97 7.86 6.28 7.18 1.39 1.58 1.78ROSH HANIQRA 0.69 0.54 2.41 1.56 10.63 10.48 8.48 1.21 1.86 1.52ELON 0.73 0.37 1.64 2.76 10.00 8.32 7.99 1.16 2.38 1.61AYYELET HASHAHAR 0.82 0.71 1.46 1.45 8.26 7.65 6.65 1.29 1.90 1.55SHAVE ZIYYON 0.64 0.74 1.84 1.47 9.90 9.64 8.76 1.24 2.12 1.53ZEFAT HAR KENAAN 0.80 0.46 1.52 3.46 8.70 5.63 6.73 1.22 1.76 1.62HARASHIM 0.66 0.43 2.26 3.66 9.17 6.23 7.57 1.19 2.46 1.64AMMIAD 0.82 0.75 1.70 1.47 8.55 8.07 6.84 1.26 2.08 1.62GAMLA 0.85 0.75 1.33 1.47 7.83 7.52 6.87 1.37 2.17 1.54ESHHAR 0.86 0.52 1.55 2.05 9.35 8.87 7.97 1.29 2.38 1.60KEFAR NAHUM 0.83 0.73 2.80 1.84 8.18 9.43 6.87 1.30 1.99 1.56BET ZAYDA 0.77 0.69 3.28 2.24 8.18 9.87 6.85 1.30 2.06 1.51DEIR HANNA 0.85 0.60 1.90 1.79 8.94 9.09 7.64 1.25 2.27 1.61AFEQ 0.81 0.69 1.48 1.48 9.56 9.96 9.02 1.32 1.99 1.58LEV KINERET 0.68 0.65 4.86 3.81 8.18 11.60 7.02 1.30 2.24 1.52AVNE ETAN 0.80 0.71 1.45 1.49 7.83 7.73 7.05 1.37 2.13 1.55HAIFA REFINERIES 0.81 0.71 1.44 1.60 9.32 10.30 9.32 1.39 1.84 1.61HAIFA TECHNION 0.68 0.63 1.81 1.92 9.32 9.18 9.48 1.39 2.49 1.65HAIFA UNIVERSITY 0.61 0.58 2.38 2.37 9.32 8.64 9.53 1.39 2.82 1.66NEWE YAAR 0.83 0.75 1.59 1.74 9.24 10.21 9.19 1.41 2.19 1.65TAVOR KADOORIE 0.79 0.75 1.25 1.23 8.72 8.69 8.19 1.32 1.88 1.59ZEMAH 0.82 0.72 2.07 1.84 8.11 9.46 7.66 1.33 1.82 1.51

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YAVNEEL 0.84 0.71 1.75 1.65 8.41 9.30 7.91 1.31 2.00 1.55MASSADA 0.81 0.74 2.13 2.06 8.04 9.42 7.76 1.36 2.26 1.52EN KARMEL 0.73 0.72 1.63 1.91 9.04 9.97 9.81 1.49 2.42 1.77MERHAVYA 0.76 0.76 1.41 1.52 8.79 9.69 9.02 1.48 1.91 1.70EN HASHOFET 0.86 0.68 1.14 1.82 8.94 9.77 9.61 1.51 2.24 1.73AFULA NIR HAEMEQ 0.62 0.78 1.86 1.74 8.79 9.90 9.12 1.48 2.13 1.71ZIKHRON YAAQOV 0.89 0.63 1.19 2.13 9.04 10.17 9.76 1.49 2.37 1.82GALED 0.87 0.72 1.17 1.75 8.96 9.70 9.65 1.50 2.32 1.76TEL YOSEF 0.74 0.77 1.28 1.37 8.56 9.39 8.99 1.41 1.72 1.71MAALE GILBOA 0.74 0.57 1.91 2.09 8.56 8.30 9.13 1.41 2.57 1.74HADERA PORT 0.82 0.77 1.68 2.08 9.22 10.73 9.63 1.43 2.25 1.85EDEN FARM 0.78 0.68 1.33 1.95 8.32 9.72 9.10 1.38 1.89 1.67SEDE ELIYYAHU 0.72 0.72 1.36 1.83 8.32 9.71 9.24 1.38 1.75 1.74EN HAHORESH 0.65 0.76 1.78 1.52 9.10 9.19 9.47 1.47 2.33 1.81N of station with better results 31 8 20 19 14 - 25 1 - 38Mean* (0 - 196 links) 0.75 0.67 1.84 1.91 8.78 9.05 8.25 1.35 2.11 1.63Mean (0.2 – 158 links) 0.77 0.67 1.81 1.91 8.78 9.05 8.31 1.35 2.11 1.85Mean (0.4 – 106 links) 0.79 0.67 1.89 1.91 8.78 9.05 8.03 1.35 2.11 2.13Name Links

IMS

ECMWF

IMS

Links

IMS

ECMWF

IMSLinks ECMWF IMS

LinksECMWF IMS

CORR RMSD MEAN STD

Note. First column: Station name; 2nd & 3rd(in parentheses) columns: Correlations (CORR) between the humidity from the 196 links (and ECMWF reanalysis) and the 39 IMS stations' humidity; 4 th & 5th columns: as for 2nd & 3rd

but for RMSD [g/m3]; Next 3 columns(6th 7th & 8th) show the MEAN humidity [g/m3] for links, ECMWF & stations; last 3 columns are as for the MEAN but for the standard deviation humidity, STD [g/m3] for the 196 links, ECMWF reanalysis and 39 IMS stations at the stations' locations. Values in bold indicate the best result. *The MEANs for all the 39 IMS stations (rows) for all the skills are given at the bottom of the table in the last three rows. The rows are for 0, 0.2 & 0.4 threshold filtering values.

4. Conclusions

The novel moisture mapping methodology introduced here as based on cellular links data was compared against meteorological stations and found to include more details. These new maps are quite similar to the observations while the ECMWF map is found to be too moist and with lower variability with primary W-E variation due to increase of the distance from the Mediterranean coast.The reasons for the variations in the computed correlations between the stations and the link-humidity fields was found to be mainly due to several factors including the local topography, distance from the coast and probably the varying density of links/stations. However, in most stations the link mapping humidity yields high correlations that are better than with the ECMWF. It should be pointed out that, indeed, the three datasets work on different spatial scales and this certainly has great influence on the skills. Since this is the first study to suggest the CML for generating humidity maps, it is our purpose to compare the skills where basic observations are available, i.e., weather stations. In the next step, the influence of link and model grid, as well as link orientations will be examined. Also, the benefit of integrating different surface moisture sources including this new CML source is of much interest.

322323324325326327328329330331332

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Filtering of several links with negative correlations obviously increases the correlation of the links-humidity fields with the stations. Examination of the "bad" links -particularly with negative correlations- shows that these specific links tend to be located above areas characterized with small-scale humidity sources like water reservoir, pool, industry, settlements and roads that are not detected by the large-scale fields as in the ECMWF. It does not mean all the aforementioned land cover features are affecting the link-humidity measurements. But eliminating the links with low correlations would overcome the problem of strong impact of local humidity sources, which do not directly impact the larger scale humidity field. Hence, a "bad" link is not necessarily bad, it is just representing the local air humidity which is missing in both the point station measurements and the corresponding ECMWF product. Consequently, these "bad" links may be used in future to examine small-scale humidity sources.

When counting the numbers of best results, it is found that links are leading compared to ECMWF in the correlations (31 vs. 8), in STD (38 vs. 1) while in the MEAN the ECMWF is doing better (14 vs. 25). The links and the ECMWF are doing similarly with the RMSD (20 vs. 19). The somewhat better STD result, i.e. 1.67 g/m3 for the links compared to observed 2.1 and 1.35 for the ECMWF, as well as the correlations for the links is obviously reflecting the higher variability obtained by the links as discussed earlier regarding the maps (Sec. 3.2). The better MEAN result achieved by ECMWF suggests that further study is required in future on the reference/median value employed in the links' methodology. However, the average MEAN differences are quite small as noticed at the bottom of Table 2, i.e., MEAN observed moisture is 9.05 as compared to 8.25 for the links and 8.78 g/m3 for ECMWF.

In conclusion, the spatial and temporal humidity variability derived from links are found to be more accurate over N. Israel. The humidity field from the links thus provides a more reliable picture of the observed moisture field as compared to the ECMWF. Consequently, the CML high-resolution humidity fields are likely to improve the numerical weather prediction results, with particular relevance to rainfall. This assumption is based on several studies that have shown that improving the input moisture in meteorological models have the high potential to improve rainfall prediction and particularly heavy rainfall events (Chen & Avissar, 1994; Fabry, 2006; Ford et al.,2015; Kunz et al., 2009). Since both mean and structure of humidity fields are important, it seems that an integration of NWP humidity fields that have some advantage in the MEAN along with CML fields will probably yield the best approach in the future.

5. Acknowledgements:

The data is found in The Israeli Atmospheric and Climatic Data Center (IACDC) in Tel Aviv Univ. Please address the corresponding author for data request.

The study was funded by the German Helmholtz Association within the DFG project. The authors are grateful to Yariv Dagan, Yaniv Koriat, Ido Inbar, Shahar Shilyan, Eli Levi and Yosi Eisenberg (Cellcom) for providing the microwave data, pro-Bono, for our research. We deeply thank our research team members (Tel Aviv University): Noam David, Hagit Messer, Yonatan Ostrometzky, Adam Eshel and Daniel Serebrenik for fruitful cooperation and discussions. Special thanks go to the Israeli Meteorological Service for the data. Thanks to the IACDC for help with obtaining the moisture IMS data.

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