Modeling cloud seeding technology for rain enhancement over the arid and semiarid areas of Ethiopia

Abstract

The government of Ethiopia has started exploring different innovative approaches to tackle the scarcity of water in arid and semi-arid regions of the country. In line with this strategy, precipitation enhancement through weather modification technology is getting strong attention and some initial attempts have been made to assess its feasibility. Therefore, this paper aims to model cloud-seeding technology for rain enhancement and check its effectiveness in arid and semiarid regions of Ethiopia. Different relevant measurements including ground-based as well as reanalysis data from 2021 to 2022 are used to improve relevant cloud-seeded models. Reanalysis data are validated with ground-based data using different error metrics. The improved cloud-seeded modeling is developed for precipitation enhancement for the arid and semiarid regions of Ethiopia. An atmospheric moisture budget is used for improving the cloud-seeded model. The results indicated that the developed model and the direct operation are well agreed upon. The relative precipitation (RP) (after the application of cloud-seeded per before the application of cloud-seeded during spring, summer, and autumn is found 1.31, 0.98, and 1.03 respectively. The changing precipitation between cloud seeded and before seeded for spring, summer, and autumn is found at 1.38, −0.19, and 0.11 mm respectively; whereas changing temperature is found at 1.08, 1.78, and −1.06 k respectively. In general, the model result indicated that cloud-seeded technology is effective over Ethiopia when the daily resultant wind speed is less than 1.5 m/s and cloud base height (CBH) is less than 1700 m. Furthermore, by observing RP from the improved cloud-seeded model results, rain enhancement science is applicable for Ethiopia during the spring and slightly autumn seasons. Hence, before artificial aerosol is seeded into the cloud, the operators should be nowcast and forecast the daily wind speed and CBH of the target area unless an economic crisis will have happened.

Graphical abstract

1. Introduction

The most important natural renewable resource on earth and in space is water, which helps life's existence [1]. Indeed, those water resources are affected by climate change due to anthropogenic and natural activities which reduced atmospheric moisture and lead to irregular occurrences of drought [1]. Therefore, the balance between the groundwater and atmospheric moisture content is demanding a sustainable future. However, at the current time, precipitation variability is unreliable occurrences in sufficient amounts in arid and semiarid regions of the Globe and severe in Ethiopia [2]. The Government's Ten-Year Perspective Development Plan (2021–2030) envisions Ethiopia as a middle-income country [3]. The ten-year plan and other national policies and strategies prioritize support for agriculture sectors and build resilience [4]. Despite considerable efforts over the past 30 years, we are not on track to achieve the Sustainable Development Goals by 2030 because of extreme events such as drought due to scarcity of water [5].

Water scarcity will be a serious environmental problem that may threaten the livelihood of hundreds of millions of people [6]. Consequently, overpopulation in developing countries such as Ethiopia will become a big environmental problem in the future. In order to feed all people to use our land as efficiently as possible. Owing to this, cloud-seeded technology is quite useful in this regard since it can help to make desert land suitable for farming [7,8]. The idea of cloud-seeded is presented in 1891 by Louis Gathmann who suggested shooting liquid carbon dioxide into rain clouds to cause them to rain. Nowadays, many countries are trying to use and adapt cloud formation and rain enhancement technology activities that are located in dry regions of the world to tackle their national problems [9]. For instance, Israel, Dakota, Korea, Arabia, India, China, Russia, Malaysia, Thailand, and the United States are involved in cloud-seeded activities [9].

A 10% increment of precipitation through cloud seeding can sustain an additional 150 000 households throughout an entire year (Abdallah and Evan, 2020). However, the lowest cost estimates published would be in the range of $27 to $53 per acre-foot (4000 m²) to produce additional precipitation [6,10,11]. Currently, Ethiopia has been experimenting with cloud enrichment technology and trying to adopt it in arid and semiarid regions of the country to increase precipitation through effective modeling. Furthermore, Ethiopia is scheduled to fill the third round of the Grand Ethiopian Renaissance Dam (GERD) in the upcoming year in 2023 [12]. Indeed, additional precipitation is a fundamental requirement to fill the third plan of GERD within a short period to bring peace to downstream countries. Therefore, this paper is aimed at modeling cloud-seeded technology for cloud enrichment effectivity in arid and semiarid regions of Ethiopia.

2. Description of the study area, data type, and methodology

2.1. Description of the study area

Ethiopia is located between 3°- 15⁰ north and 33⁰ - 48⁰ east in the horn of Africa which is bounded by Eritrea to the north, Djibouti to the north-east, Somalia to the east, Kenya to the south, and, south-Sudan to the west [13,14] as shown in Fig. 1. It covers an area of about 1.14 million km² [14]. The elevation ranges from 160 m below sea level to over 4600 m above sea level of northern mountainous regions [13]. The highest mountains are concentrated on the northern and southern plateaus of the country [14]. A large percentage of the country consists of high plateaus and mountain ranges, dissected by major rivers such as Blue Nile, Tekeze, Awash, Omo, Wabi Shebelle, etc with a total of 9 major rivers and 19 lakes [13]. The Blue Nile, the chief headstream of the Nile, rises in Lake Tana in Northwest Ethiopia.

Fig. 1.

Latitudinal and longitudinal Location of Ethiopia (Enyew and Steenyeld, 2014).

Ethiopia is the most second popular country in sub–Saharan Africa with an estimated population of about 120 million which is mostly distributed in the northern, central, and southwest highland areas [15]. Around 88% of the population is living in highland areas with a population density of 141 people per km² [15]. Significant precipitation is found from June to September and the annual average value of 900 to 2200 mm [5]. Around 85% of the Ethiopian population depends on rain-fed agriculture (Amare and Abebe, 2019). The nation is blessed with a unique landscape that varies in terrain, temperature, and biological resources and includes everything from mountains and marshes to valleys and deserts [16]. With such variety, there is a chance that conditions for human habitation will be good. However, agriculture is small-scale and subsistence-oriented, and it is in a very low state of development [17]. As a result, the country has become one of the poorest nations in the world and has often failed to be self-sufficient in food [18]. Drought is a critical climate-related hazard in Ethiopia, frequently occurring in many parts of the country [19]. The areas are highly vulnerable, desertification and drought have been persistent problems throughout history, with associated food shortages and famine [18,20].

2.2. Data sources

Precipitation (P) data were obtained from National Meteorology Agency (NMA) from 34 different stations from 2021 to 2022 to validate the model data. The spring, summer, and autumn seasons are considered for this study because winter didn't have cloud cover and cloud-seeding is not practiced. These stations are selected to consider the arid and semiarid locations. Ethiopia is one of data scarce regions due to less density and irregular orientations of in-situ measurements (Abera et al., 2017). In order to tackle this challenge, different gridded P, temperature (T), evaporation (E), surface pressure (Ps), total cloud cover (Tcc), dew point temperature (DT), zonal wind (U), and meridional wind (V) at 10 m altitude data were obtained from the European Center of Medium-range Weather Forecast (ECMWF) reanalysis products from 2021 to 2022 for validation and cloud-seeding modeling. Furthermore, P data were obtained from an improved Regional Climate Model (RCM), Climatic Research Unit (CRU), and Climate Hazards Group Infrared Precipitation combined with Station (CHIRPS) [21]. Cloud-seeded P, location, and date data are found from Ethiopian Information Network Security Agency (INSA) from 2021 to 2022. The temporal and spatial resolution of the data from different models is provided in Table 1.

Table 1.

The data sources, spatiotemporal resolution, time coverage, and website.

Data types	Data used	Time resolution	Space resolution	Website
Gauge	2021–2022	daily	34 stations per UBNB	non
CRU	2021–2022	monthly	0.5 by 0.5°	http://www.cru.uea.ac.uk
RCM	2021–2022	daily	0.11 by 0.11°	http://www.data.euro-cordex.net
CHIRPS	2021–2022	daily	0.05 by 0.05°	www.CHIRPS-2.0/global_daily
ECMWF	2021–2022	daily	0.125 by 0.125-degree	https://cds.climate.copernicus.eu

The data is analyzed using MATLAB and Climate Data Operator (CDO) software.

2.3. Methodology

The performance of different satellite blended reanalysis data is validated by in-situ data using different error metrics such as bias ratio (BR), correlation and regression coefficient (R), root means squared error (RMSE), mean relative error (MRE), and Pearson correlation coefficient (P) to select appropriate data for cloud-seeded modeling. In-situ (station) data that has distinct missing values and are filled using weighted average interpolation techniques. The performance of the cloud-seeded model is checked with the direct application of ground filers and airplane areal operation of the southern and northern parts of Ethiopia from 2021 to 2022 during the spring and autumn season. The improved cloud-seeded model is formulated from equations (1), (2), (3), (4), (5), (6), (7), (8), (9), (10), (11), (12), (13), (14), (15), (16)) [22].

$$P_{ch\left(T,P\right)}=P_{cs\left(T,P\right)}-P$$ (1)

Where P_ch is the change in precipitation after cloud-seeded in the function of pressure and temperature, P_cs is cloud-seeded precipitation, and P is the natural precipitation in the target area with the assumption of non-seeded operation. Equation (2) is the existing model of the cloud-seeding technology which is supported by Refs. [11,23,24].

$$P_{cs}\left(T,P_r\right)=\left(1-N_{ccn}\right)+N_{ccn}P$$ (2)

where N_ccn is the number of concentration active cloud condensation nuclei which is written as in equation (4) [[25], [26], [27]].

Our new modified model is considering the atmospheric moisture budget for cloud-seeding technology as shown in equation (3) and to simplify this equation to get the final model is written as equation (16). Without atmospheric moisture, the cloud may not be developed and we didn't apply artificial aerosols such as silver chloride, sodium chloride, and Potassium iodide.

$$P_{cs}\left(T,P_r\right)=M\left(1-N_{ccn}\right)+N_{ccn}P$$ (3)

where M is the amount of atmospheric moisture budget which is equivalent to outflow per target area (evaporation) minus inflow per target area (P) [21].

$$N_{ccn}=n_o{\left[\left(s_i-1)/(s_w-1\right)\right]}^b\exp \left(\beta \Delta T\right)$$ (4)

Where $\beta$ = 0.6⁰C⁻¹, b = 4.5, n_o = 10⁻⁵ l⁻¹, $\Delta T$ = T_o-T is the degree of supercooling, $T_o$ = 273.15 K is surface temperature, T is 2 m altitude temperature, S_w is super-saturation concerning water, and S_i is super-saturation concerning ice [23,24].

$$Q_{is}=3.8{P_r}^{-1}x{10}^{9.5\left(\frac{T-273}{T-8}\right)}$$ (5)

$$Q_{ws}=3.8{P_r}^{-1}x{10}^{7.5\left(\frac{T-273}{T-6}\right)}$$ (6)

where $Q_{is}$ is the saturation mixing ratio over ice, $Q_{ws}$ is the saturation mixing ratio over water, P is atmospheric pressure, and Q_v is the water vapor mixing ratio in the air which is equal to 40 g/kg = 0.04 kg/kg [28,29].

The pressure at height (h) is estimated by using the surface temperature (T_o) using the following equations.

$$P_r=P_{or}{\left(1+\frac{T}{\gamma z}\right)}^{g/R_a\gamma }$$ (7)

where P_r is the pressure at height h, P_or is standard atmospheric pressure taken to be 1.01 × 10⁵ Pa, Ra = 287 Jkg⁻¹k⁻¹ is gas constant, Gamma ($\gamma )$ is 6.5 ⁰c.km⁻¹. The young cumulus clouds are chosen for seeding with a depth of about 6000 feet which did not precipitate naturally (Breed et al., 2014). Then S_w and S_i can be calculated by using Karimpirhayati [30], Al Hosari et al. [31], and Abbate et al. [32] formula.

$$\begin{array}{l}{S}_w=\frac{Q_V}{Q_{ws}}-1\\ \end{array}$$ (8)

$$S_i=\frac{Q_V}{Q_{is}}-1$$ (9)

Substituting equations (8), (9)) to equation (3), we obtained

$$N_{ccn}=n_o{\left[\frac{\frac{Q_V}{Q_{is}}-1-1}{\frac{Q_V}{Q_{ws}}-1-1}\right]}^b\exp \left(\beta \Delta T\right)$$ (10)

$$N_{ccn}=n_o{\left[\frac{\frac{Q_V}{Q_{is}}-2}{\frac{Q_V}{Q_{ws}}-2}\right]}^b\exp \left(\beta \Delta T\right)$$ (11)

$$N_{ccm}=n_o{\left[\left(\frac{Q_V}{Q_{is}}-2\right)/\left(\frac{Q_V}{Q_{ws}}-2\right)\right]}^b\exp \left(\beta \Delta T\right)$$ (12)

$$N_{ccn}=n_o{\left[(\frac{Q_V-2Q_{is}}{)}/(\frac{Q_V-2Q_{ws}}{Q_{ws}})\right]}^b\exp \left(\beta \Delta T\right)$$ (13)

$$N_{ccn}=n_o{\left[\left(\frac{Q_V-2x3.8{P_r}^{-1}x{10}^{9.5\left(\frac{T-273}{T-8}\right)}}{3.8{P_r}^{-1}x{10}^{9.5\left(\frac{T-273}{T-8}\right)}}\right)/\left(\frac{Q_V-2x3.8{P_r}^{-1}x{10}^{7.5\left(\frac{T-273}{T-6}\right)}}{3.8{P_r}^{-1}x{10}^{7.5\left(\frac{T-273}{T-6}\right)}}\right)\right]}^b\exp \left(\beta \Delta T\right)$$ (14)

$$N_{ccn}=n_o{\left[\left(\frac{Q_V}{3.8{P_r}^{-1}x{10}^{9.5\left(\frac{T-273}{T-8}\right)}}-2\right)/\left(\frac{Q_V}{3.8{P_r}^{-1}x{10}^{7.5\left(\frac{T-273}{T-6}\right)}}-2\right)\right]}^b\exp \left(\beta \Delta T\right)$$ (15)

Equation (15) substitutes to equation (2) we obtained equation (16).

$$\begin{array}{l}{P}_{cs\left(T,P_r\right)}=M\left(1-n_o{\left[\left(\frac{Q_V{P}_r}{3.8x{10}^{9.5\left(\frac{T-273}{T-8}\right)}}-2\right)/\left(\frac{Q_V{P}_r}{3.8x{10}^{7.5\left(\frac{T-273}{T-6}\right)}}-2\right)\right]}^b\exp \left(\beta \Delta T\right)\right)+\\ n_o{\left[\left(\frac{Q_V{P}_r}{3.8x{10}^{9.5\left(\frac{T-273}{T-8}\right)}}-2\right)/\left(\frac{Q_V{P}_r}{3.8x{10}^{7.5\left(\frac{T-273}{T-6}\right)}}-2\right)\right]}^b\exp \left(\beta \Delta T\right)P\end{array}$$ (16)

Equation (16) is our new simulation model to estimate precipitation and equation (1) is helpful full the change in precipitation before and after artificial aerosol is added.

The cloud base height (CBH) could be calculated using standard temperature (T₀) and dew point temperature (DT) [33].

$$CBH=\left(\frac{T_0-DT+273.15}{2.5}\right)*1000$$ (17)

Relative precipitation (RP) is calculated by equation (18) and suggested by Zhao and Lei [34], Daniel et al. [35], and Wu et al. [36].

$$RP=\frac{P_{cs\left(T,P\right)}-P}{P}$$ (18)

If the RP is greater than 1, it would indicate a positive effect of seeding [35,36]. The Effectivity (Ef) is determined by the number of cloud-seeded operation effective dates (Ned) multibladed by hundred (100) per number of cloud-seeded operation trials (Nt). Several consecutive dates must be considered when the P observation with and without cloud-seeded variation. Having P at x date without seeded and having P at date y by the application of the seeded and comparing with them their amounts. Mathematical is formulated by

$$Ef(\%)=\frac{Ned}{Nt}x100$$ (19)

3. Results

3.1. Reanalysis model data performance analysis with the ground-based observation

The performances of reanalysis data (ECMWF, CHIRPS, CRU, and RCM) with the daily mean spatial average value of ground-based data from 2021 to 2022 are analyzed as shown in Fig. 2.

Fig. 2.

Comparisons of reanalysis precipitation (P) daily mean of monthly data (ECMWF indicated by black color, CHIRPS by green color, CRU by red color, and RCM by cia color) with gauge data. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)

The daily mean of monthly reanalysis precipitation data compared to the weighted average interpolation of the daily mean of monthly ground-based direct observation is provided in Fig. 2. Hence, CRU and CHIRPS-derived P observations have good comparisons with the ground-based direct observations except for July and August. The daily mean of monthly observation CRU and CHIRPS data are overestimated during the heavy rainy season compared to the ground-based observation as shown in Fig. 2. The ground-based observation of CRU, CHIRPS, RCM, and ECMWF peak P is observed in July and August as shown in Fig. 2. From July–December, RCM P estimation was slightly overestimated compared to the ground-based, ECMWF, and CRU. CRU daily P is underestimated during light and moderate rain months (January to June); while RCM P data is overestimated during moderate rain months (October to December) see Fig. 2. The daily mean of monthly precipitation from ECMWF is quite similar to the ground-based observation as shown in Fig. 2 and Table 2.

Table 2.

The reanalyzes model performance based on different error metrics with ground-based P data.

Gauge	BR	MRE	RMSE	R	P
ECMWF	0.99	–0.01	0.70	0.84	0.03
CHIRPS	1.11	0.10	3.82	0.76	0.04
RCM	1.05	0.08	1.83	0.73	0.04
CRU	1.01	0.04	0.93	0.56	0.06

The error metrics (R, BR, MRE, RMSE, and P) between the ground-based direct observation and ECMWF P data are found 0.84, 0.99, −0.01, 0.70, and 0.03, whereas CHIRPS are found 0.76, 1.11, 0.10, 3.82, and 0.04, while with CRU is 0.56, 1.01, 0.04, 0.93, and 0.06 respectively. Similarly, ground-based RCM is found at 0.73, 1.05, 0.08, 1.83, and 0.04 respectively. This shows that ECMWF is very close to the ground-based observed value, while CRU deviated from the ground-based data value compared to the rest model values with a statistically insignificant correlation since the P value is greater than 0.05 which is 0.06 as we can see from Table 2. Hence, ECMWF model data are used for the cloud-seed modeling due to the best agreement with the ground-based data compared to RCM, CHIRPS, and CRU. Except for RCM, ECMWF, CHIRPS, and CRU are validated by Megbar et al. [21]and found near similar results. Hence this paper strongly supported the previous findings which were done by Megbar et al. [21].

3.2. The spatial variability of zonal wind speed (U), meridional wind speed (V), precipitation (P), and temperature (T) for the spring season from the ECMWF model before cloud seeding application

During the spring (MAM represented March, April, and May) season, the spatial variability of U, V, P, and T from the ECMWF model before weather modification is provided in Fig. 3. In Ethiopia, the range of U varied from −4 to 2 m/s with a spatial average value of −1.5 m/s. The negative sign is indicated as the dominant wind flows from east to west (negative x-axis). It is agreed to the natural dynamics since during the spring season the western part of the Globe tilts towards the sun [21,31,37,38]. Therefore, a high-pressure gradient force developed in the eastern part of the Globe, and the wind blows from east to west as shown in the top left panel of Fig. 3. As a result, the central highland area of Ethiopia has found moderate P as shown in the bottom left panel of Fig. 3. Similarly, the V varied from −5 to 5 m/s with a spatial average value of −1.3 m/s. The negative sign indicated as the resultant V direction is north to south (negative y-axis). The magnitude of V is found maximum in the southeast parts of Ethiopia since relatively less mountainous areas are not blocked by the wind that blows from north to south as shown in the top right panel of Fig. 3. Similarly, the T value varied from 290 to 310 k with a spatial average value of 298.34 k. The maximum value of the P and the minimum value of T is found in the central parts of the study area which is a mountainous area (see bottom left and right panels of Fig. 3). Before the application of cloud-seeding modification, P varied from 0 to 15 mm with a spatial average value of 4.51 mm.

Fig. 3.

The spatial distribution of U (east to west or west to east) wind at the top left panel, V (north to south or south to north) wind speed at the top right panel, natural P at the bottom left panel, and T during spring season MAM (March, April, and May) at the bottom right panel observation. The black arrowhead of the top right and left panels are represented wind direction.

The vertical velocities within the base of cumulus clouds experience strong spatial and time variations. According to Ellen and Asgeir [5] explanation, during the spring season the wind blows from the Indian Ocean and converges above the southern half of Ethiopia. Hence, this paper's analysis is similar to the study conducted by Ellen and Asgeir [5].

During the summer season JJA (June, July, and August) the spatial distribution of U, V, P, and T from the ECMWF model before cloud-seeded is displayed in Fig. 4.

Fig. 4.

The spatial distribution of U for summer at the top left panel, V for summer at the top right panel, natural P for summer at the bottom left panel, and T for the summer season at the bottom right panel observation.

During summer, U varied from −1 to 5 m/s with a spatial average value of 2.10 m/s. The positive average value is indicated as the dominant wind flows from west to east (positive x-axis). The other wind parameter V varied from −5 to 8 m/s with a spatial average value of 2.20 m/s. The positive average value indicated as the resultant V direction is south to north (positive y-axis). It is agreed to the natural dynamics since during the summer season the northern part of the Globe tilts to the sun [38]. Therefore, a high-pressure gradient force developed in the southern part of the Globe, and the wind blows from southwest to northeast as shown in the top left panel of Fig. 4 and supported by different literature [21,37].

During autumn (September–November (SON)), the spatial dynamics of U, V, P, and T are displayed in Fig. 5.

Fig. 5.

The spatial distribution of U for autumn at the top left panel, V for autumn at the top right panel, P for autumn at the bottom left panel, and T for autumn at the bottom right panel from 2021 to 2022 over Ethiopia.

During autumn from 2021 to 2022, the value of U varied from −1 to 5 m/s with a spatial average value of −0.71 m/s. The negative average value indicated the dominant wind flows from the northeast to the southwest direction. Similarly, the V wind varied from 0 to 6 m/s with a spatial average value of 2.92 m/s. During the autumn season, the moisture-carrying wind blows from the Red Sea to Ethiopia and the northern part of the country received moderate P as shown in Fig. 5. Hence P varied from 0 to 8 mm with an average value of 3.25 mm; whereas T varied from 280 to 310 k with an average value of 294.13 k which is minimum in its magnitude compared to the rest seasons and supported by the study conducted by Zangvil et al. [39], Zhang et al. [40], and Zheng et al. [41]. The seasonal dynamics estimation of U, V, P, and T is quite useful to apply to cloud seeding technology. Furthermore, cloud base height (CBH), dew point temperature (DT), evaporation/atmospheric moisture (E), total cloud cover (Tcc), and P and T are substantially applicable for cloud-seeded model performance improvement [10,11].

3.3. The spatial and seasonal variability estimation of Tcc, E, CBH, and DT after applying cloud-seeded modeling

The rain enhancement technology modeling needs convective clouds and cloud base height that is E occurs to condense as it rises, and the DP which measures the humidity content in the air is displayed in Fig. 6.

Fig. 6.

After cloud-seed spatial distributions of the spring season total cloud cover (Tcc) are represented at the top left panel, evaporation (E) at the top right panel, cloud base height (CBH) at the bottom left panel, and dew point temperature (DT) at the bottom right panel over Ethiopia from 2021 to 2022.

After cloud-seeded precipitation (Pcs), change in precipitation (Pch) (the difference between Pcs and natural precipitation (P)), the cloud-seeded temperature (Tcs), and change temperature (Tch) (the difference between the temperature after cloud-seeded and before) are provided at Fig. 7.

Fig. 7.

Spatial distributions of cloud-seeded precipitation (Pcs) at the top left panel, change in precipitation (Pch) the difference between cloud-seeded precipitation and natural precipitation at the top right panel, cloud-seeded temperature (Tcs) at the bottom left panel, and change in temperature (Tch) at the bottom right panel during spring over the study area and the study period.

During spring, the atmospheric moisture budget (E) varied from 1 to 5 mm with a spatial average value of 3.20 mm. Based on this moisture budget, the CBH varied from 1000 to 8000 m with an average value of 3100 m. The maximum value of Tcc is found at 1700 m CBH. Therefore, we observed the Pcs are effective at the CBH is less than 1700 m, and E is found at a moderate level as shown in Fig. 6, Fig. 7. The amount of E is too much high the cloud-seeded is not recommended since the rain may fall naturally without adding manmade aerosols to the cloud. The DT is enhanced when the artificial aerosol is added to the atmosphere because of weather modification and it helps to accelerate the cloud condensation rate as shown in the model outputs in Fig. 6, Fig. 7. Pcs varied from 0 to 20 mm with an average value of 5.89 mm; while P_ch varied from −8 to 10 mm with an average value of 1.38 mm. The change in P is positive when the daily U and V wind value become decreased. Furthermore, the cloud-seeded technology is effective when the daily average magnitude value of U and V is less than 1.5 m/s (Pch is positive) during the spring season as we can see in Fig. 3, Fig. 7, and Table 3. After an aerosol is seeded to the cloud the air temperature (Tcs) varied from 290 to 310 k with an average value of 299.42 k, while T_ch varied from −10 to 10 k with an average value is found 1.08 k which indicated that the number of aerosol concentration is increased in the atmosphere the air T is increased because of absorption and reflection of outgoing radiation towards the earth's surface as shown in Fig. 7.

Table 3.

The range and the spatial mean value of P, Pcs, Pch, T, Tcs, Tch, U, and V during spring, summer, and autumn based on the model result from 2021 to 2022 over Ethiopia.

Parameter	Minimum	Maximum	Average
Spring
P (mm/day)	0	15	4.51
Pcs (mm/day)	0	20	5.89
Pch (mm/day)	−8	10	1.38
T (k)	290	310	298.34
Tcs (k)	290	310	299.42
Tch (k)	−10	10	1.08
U (m/s)	−4	2	–1.50
V (m/s)	−5	5	–1.30
Summer
P (mm/day)	5	40	8.33
Pcs (mm/day)	5	35	8.14
Pch (mm/day)	−0.80	0.61	–0.19
T (k)	280	305	295.76
Tcs (k)	290	310	296.64
Tch (k)	−10	12	1.78
U (m/s)	−1	5	2.10
V (m/s)	−5	8	2.20
Autumn
P (mm/day)	0	8	3.25
Pcs (mm/day)	0	8	3.34
Pch (mm/day)	−0.40	0.60	0.11
T (k)	280	310	294.13
Tcs (k)	275	305	292.07
Tch (k)	−15	10	–1.06
U (m/s)	−1	5	–0.71
V (m/s)	0	6	2.92

The spatial distribution of Tcc, E, CBH, and DT is observed during the summer (JJA) after applying artificial aerosol on the cloud over Ethiopia as shown in Fig. 8.

Fig. 8.

The spatial distribution of Tcc at the top left panel, E at the top right panel, CBH at the bottom left panel, and DT at the bottom right panel of the summer season before cloud-seeded application over Ethiopia from 2021 to 2022.

During the summer the spatial variation and affectivity of cloud-seeded technology on atmospheric parameters are provided in Fig. 9.

Fig. 9.

The spatial distribution of Pcs at the top left panel, the Pch at the top right panel, Tcs at the bottom left panel, and Tch at the bottom right panel is observed after the seeding of artificial aerosol to the cloud during the summer season.

During the summer, E varied from 2 to 6 mm with an average value of 3.7 mm. Indeed, the maximum amount of T_cc found southwest and western parts of the study area at the CBH is less than 2000 m. Similarly, the maximum and minimum DT is found in the western and northeast parts of the study area respectively. The minimum value of DT is found at CBH greater than 6000 m, whereas the maximum value is found at CBH less than 1600 m. In the same cause, E is found to be high at CBH of less than 2000 m as shown in Fig. 9. Owing to this, the P_cs varied from 5 to 35 mm with a spatial average value of 8.14 mm, while P_ch varied from −0.80 to 0.61 mm with an average value of −0.19 mm. In the same method, Tcs varied from 290 to 310 k with an average value of 296.64 k, whereas T_ch varied from −10 to 12 k with a spatial average value of 1.78 k as shown in Fig. 9 and Table 3. T is enhanced when an additional aerosol is added to the atmosphere for the purpose of rain enhancement as shown in Fig. 7, Fig. 9. In the central part of Ethiopia, the positive value of T_ch is observed. The model results indicated that during the summer, cloud-seeded technology is not applicable in Ethiopia but rather enhanced the T. However, it is quite useful for aviation because high fog, drizzle, and hail are occupied by the sky throughout the day, and the flight is not suitable for a flier. Hence, by applying artificial aerosol to the fog or cloud converted to P within a short period the sky is clear and suitable for flight. Furthermore, cloud seeded is applicable to control snowfall since snow occurs when the cloud is formed above 2000 m altitude and takes a significant time to reach the maximum thermodynamic point but artificial aerosol is seeded to the atmosphere of the cloud before reaching maximum altitude, and the thunderstorm converted to P within a short period that is the reason the model outputs of Pch is negative during the summer season as shown in Fig. 7, Fig. 9. Similar results were also reported by several authors [8, 10, 24, 42, 43].

During autumn, the spatial distribution of Tcc, E, CBH, DT, Pcs, Pch, Tcs and are displayed in Fig. 10, Fig. 11.

Fig. 10.

Spatial distribution of Tcc at the top left panel, E at the top right panel, CBH at the bottom left panel, and DT at the bottom right panel before the application of cloud-seeded model operation during autumn (September, October, and November (SON)) over Ethiopia.

Fig. 11.

The spatial distribution of Pcs at the top left panel, Pch at the top right panel, Tcs at the bottom left panel, and Tch at the bottom right panel after the application of cloud-seeded modeling during autumn from 2021 to 2022 over Ethiopia.

During the autumn/SON, strong T_cc is found in the southwest part of the study area. A nearly similar distribution is observed at E in the western and northwest parts of the study area. The CBH is effective for T_cc formation which is valued at 1500 m which is less than 2000 m for all seasons. The Pcs varied from 0 to 8 mm with an average value of 3.34 mm; whereas P_ch varied from −0.40 to 0.60 mm with an average value of 0.11 mm. Sightly enhancement of P is observed because of cloud-seeded. Similarly, Tcs varied from 275 to 305 k with an average value of 292.07 k; whereas the T_ch varied from −15 to 10 k with an average value of −1.06 k as shown in Table 3. The Tch average value in autumn is negative since the relatively autumn season is too cool in most parts of Ethiopia and adding additional aerosol to it becomes more condensed and the cloud converted to ice that reflected and attenuated the incoming short-wave radiation before reaching the ground as shown in Fig. 11. The P_ch is positive in the northeast part of the country due to the minimum V wind speed whose value is nearly zero (see the top right pane of Fig. 4). Whereas in the remaining part of the country U and V are significant and it moves the cloud outside the study area before it reaches the condensation rate.

During the spring, summer, and autumn seasons, the cloud-seeded technology is effective over Ethiopia when the daily U and V wind speed is less than 1.5 m/s and CBH is less than 1700 m. Furthermore, as the model results indicated cloud-seeded is applicable for Ethiopia during the spring and slightly autumn seasons in the study period. However, cloud-seeded technology is not applicable when the daily U and V wind speed is greater than 1.5 m/s as shown in Fig. 3, Fig. 4, Fig. 5, Fig. 6, Fig. 7, Fig. 8, Fig. 9, Fig. 10, Fig. 11. Various studies reported that the P rate increases from seeding over a range of about 0.10 to 1.00 mm per hour [35].

3.4. The error metrics between cloud-seeded precipitation from the developed model and the direct operation (the direct practice of the aerosol-seeded to the cloud) and seeded effectiveness based on the relative precipitation (RP)

The error metrics such as P, R, BR (cloud-seeded precipitation (Pcs) from the developed model and the direct trial Pcs), MRE, RMSE, and RP (cloud-seeded precipitation per unseeded precipitation) during spring, summer, and autumn are provided in Table 4.

Table 4.

The performance of cloud-seeded model Pcs output is checked by the direct operation (direct practice of the aerosol-seeded to the cloud) of the average value of 2021 and 2022 spring and autumn seasons using different error metrics such as Pearson correlation coefficient (P), bias ratio (BR) (Pcs from the model per Pcs from the direct operation), relative precipitation (RP) (Pcs per before the application of cloud-seeded (P)), mean relative error (MRE), root mean squared error (RMSE), and correlation coefficient (R) during spring, summer, and autumn from 2021 to 2022 over Ethiopia.

Season	P	BR	MRE	RMSE	R	RP
MAM	0.01	1.01	0.40	1.34	0.86	1.31
JJA						0.98
SON	0.05	1.08	0.83	2.73	0.74	1.03

The P, BR, MRE (in millimetres), RMSE, R, and RP between the developed cloud-seeded model and the direct trial of the cloud-seeded operation through plane and ground-based generator are found to be 0.01, 1.01, 0.40, 1.34, 0.86, and 1.31 for spring; while 0.05, 1.08, 0.83, 2.73, 0.74, and 1.03 for autumn season respectively. During the summer, haven't direct operation cloud-seeded data but RP is found 0.98. The model is well agreed to the direct operation with the statistical P value is less than 0.05 which is 0.01 is significantly correlated. The study conducted by Abdallah and Evan [1] and Al Hosari et al. [31] indicated as the RP is greater than 1 the cloud-seeded is effective and useful; while RP is less than 1 is not effective or economic waste happened. According to such kinds of literature, in this paper during the spring season, PR is found 1.31 which is greater than 1 the seeding is effective, and during autumn is slightly effective but not recommended. Hence, the developed cloud-seeded model is effective for Ethiopia during the spring season as confirmed by our model results based on the error metrics during the study period. Hence this model is quite useful for technologists, meteorologists, and cloud-seeded operators. According to the study conducted by Abdallah and Evan [1] and Mazur [8], cloud-seeded technology is effective for the summer season in the Bristol region. Contrarily, in this finding, the cloud-seeded is more effective during spring and slightly effective during autumn. The difference may be associated with the location/hydro-climate of the study area.

3.5. The spatiotemporal P estimation for cloud-seeded direct operation effectivity over northern, central, and southern Ethiopia from 2021 to 2022

The spatial and temporal cloud-seeded operation effectivity during spring 2021 is provided in Fig. 12. The operation is experimenting in northern and central Ethiopia as shown in the white square on the spatial plots of Fig. 12. Relatively, the operation part of Ethiopia has gotten moderate precipitation than the rest. During the march, the operation is practiced on days 19, 22, 24, 26, and 31, 2021. From the 5 trial days, the effective date is 1 at date 19, and the effectivity is found 20% which is calculated by equation (19). In the trial days, the corresponding wind speed is greater than 1.5 m/s excluding day 19 which is 0.8 m/s as shown in the top time series panels of Fig. 12. During April 2021, the number of trial operation days is 12 (5, 6, 11, 12, 18, 22, 23, 25, 26, 27, 28, and 29) and the number of effective days is 4 which is day 12 is effective by 0.5, 26, 27, and 29 with the total effectivity of 29.16%. On the effective date of 12, 26, 27, and 29 cloud-seeded operation, daily wind speed is 1.7, 0.0, 0.9, and 0.8 m/s respectively. Except for date 12, the daily wind speed is less than 1.5 m/s which agreed with the developed cloud-seeded model results. During May 2021, the number of trial days is 4 (1, 2, 3, and 4) and the effective days of the seeded operation are 2 (at day 2 the effectivity is weighted by 0.5 since the operation is not significant amount and day 3 is much effective) and the total effectivity is found at 37.50% as shown in Fig. 12 below spatial panels. In the two cloud-seeded effective dates, the daily wind speed is found zero.

Fig. 12.

The spatial and temporal cloud-seeded operation effectivity during spring (March 19, 22, 24, 26, 31; April 5, 6, 11, 12, 18, 22, 23, 25, 26, 27, 28, and 29; May 1, 2, 3, and 4, 2021) and impact of daily winds on its effectivity over northern and central Ethiopia.

During the spring of 2022, the cloud seeding operation is experimenting with in central and southern Ethiopia as shown by the white square line on the spatial panels of Fig. 13. The operation is more effective in southern Ethiopia than in the rest.

Fig. 13.

The spatial and temporal cloud-seeded operation effectivity during spring (March 16, 17, 18, 19, 20, 21, 22, 23, 25, and 26; April 1, 2, 3, 10, 11, 13, 14, 15, 16, 17, 18, 19, 20, 21, 24, 25, 26, 27, 28, and 30; May 1, 2, 3, 4, 7, 8, 9, 11, 17, 18, 21, 22, 24, and 25, 2022) and impact of daily winds on its effectivity over southern and central Ethiopia.

During March 2022, the number of operation trial days is 10 (16, 17, 18, 19, 20, 21, 22, 23, 25, and 26) and the number of effective days is 3 (18, 19 is effectivity value weighted to 0.7, and 23) with the effectivity of 27.27%. Except for days 19 and 23, the magnitude of the daily wind speed is greater than 2 m/s as shown in the top time-series panels of Fig. 13. During April 2022, the number of cloud-seeded trial days is 20 (1, 2, 3, 10, 11, 13, 14, 15, 16, 17, 18, 19, 20, 21, 24, 25, 26, 27, 28, and 30) and the number of effective days is 5 (date 10 and 11 weighted to each by 0.5 the others 16, 24, and 28 is weighted to each 1) with the total effectivity of 20.00% as shown the table which is found into Fig. 13. Similarly in May 2022, the number of cloud-seeded trial days is 14 (1, 2, 3, 4, 7, 8, 9, 11, 17, 18, 21, 22, 24, and 25) and the number of effective cloud-seeded operation days is 2 (date 2 and 24) with the monthly effectivity is found 14.29% as shown the table within Fig. 13. A daily wind speed from all trial days except date 2 is 0 and date 24 is 0.9 m/s the remaining is greater than 1.5 m/s as shown in the bottom panels of the time series in Fig. 13.

During autumn 2022, the cloud-seeded operation is practiced in the southern parts of the country which is laying the white box on the spatial plots displayed in Fig. 14. During September, the number of trial days is 22, 26, and 28 and the number of effective days is zero with an effectivity of 0.00%. During October, the number of trial days is 4, 5, 6, 17, and 27 and the number of effective days is 4 by 0.5 and 27 with an effectivity of 30.00% as shown in the table located in side Fig. 14.

Fig. 14.

The spatial and temporal cloud-seeded operation effectivity during autumn (September 22, 26, and 28; October 4, 5, 6, 17, and 27; November 3, 4, 5, 11, and 14, 2022) and impact of daily winds on its effectivity over southern Ethiopia.

4. Discussion

According to the study conducted by Zangwill et al. (2004), more than 75% of the Ocean is found in the southern hemisphere, and southerly winds are rich in moisture that contributed significant P in the western part of Ethiopia as shown a bottom left panel of Fig. 4. Hence, P varied from 5 to 40 mm with a spatial average value of 8.33 mm which is significant compared to the spring season. As a result, from the central highland area of Ethiopia to the west has found significant P (reaches 40 mm) as shown in the bottom left panel of Fig. 4. Similarly, in the summer season, the intensity of U and V is too high in the southeast of Ethiopia since relatively less mountainous area as shown in the top right panel of Fig. 4. Owing to this, the T value varied from 280 to 305 k with a spatial average value of 295.76 k. The minimum value T is found in the western part to the central parts of the study area which is a mountainous area (see bottom right panel of Fig. 4). Owing to this, the results strongly agreed with the general dynamics. Furthermore, the moisture dynamics of the summer season are different from the spring season since the wind blows from southwest to northeast and carries moisture from the Atlantic Ocean during the summer season as shown in the black arrowheads in the right and left panels of Fig. 4. This kind of literature was studied by Korecha and Barnston [16] also supported to our study. Beyond this, previous studies indicated that Atlantic Ocean moisture contributed maximum P than the Indian Ocean [16].

The height dependence of Nccn is very important in case droplets attributed to the activation of cloud nuclei by dry air entrainment and mixing with cloud surroundings at significant distances above the cloud base [8]. The cumulus clouds can easily reach heights of several kilometers. The Nccn, especially larger ones, decreases significantly with height. Many authors such as Rhett [10], and ENMOD [11] stated that Nccn is effective for cloud-seeded when the CBH is found at 1500 m. Our model results nearly agreed with the literature indicated above which is the CBH is less than 1700 m. It is well known that updraft speed at the CBH determines the maximum supersaturation and droplet concentration in the vicinity of the CBH. When the additional artificial aerosol is added to the cloud and it is effective to mature the cloud as shown in the bottom right panel of Fig. 7. In the southwest part of the country, the DT is increased. Similarly, the E is increased as shown top right and bottom right panels of Fig. 6. When the humidity content in the atmosphere is high and DT becomes increased [10]. Hence, the study conducted by Rhett [10] strongly agreed with this finding. Indeed, the maximum daily summer season Tcc is observed in the southern part of Ethiopia compared to the remaining parts of Ethiopia. The evaporation also increased in the south and southwest parts of Ethiopia (Fig. 8).

In this paper, the model is greatly supported by the pieces of literature, which found seeded precipitation of 0.21 to 0.83 mm per hour with an average value of 0.52 mm per hour during the spring season. We observed from Fig. 11 that when Nccn dispersed into the supercooled liquid water (SLW) the Pcs increased from 10 to 40 mm per day. After cloud-seeded, the Pch data accumulated daily spring season products. Therefore, our results have coincided with previous studies [31,34]. Many authors such as Rhett [10], and ENMOD [11] stated that cloud-seeded is effective when the CBH is found at 1500 m. Hence, our model results nearly agreed with the literature indicated above which is the CBH is less than 2000 m (spring is 1700, summer is 1600, and autumn is 1500) as shown in Table 3. The seasonal effectivity of spring 2021, spring 2022, and autumn 2022 is found at 28.79%, 20.52%, and 18.33% respectively. The effective date is compared with unseeded dates of the direct cloud-seeded operation to check the performance of the developed cloud-seeded simulation model which is written in equation (16) and is found to encouragebel.

5. Conclusions and recommendations

Cloud-seeded studies are a basic tool to understand, hydrological and climatological aspects of the atmosphere that helps us desert areas make suitable for farming. Therefore, this paper is aimed to estimate artificial weather modification (cloud-seeded precipitation) over Ethiopia from 2021 to 2022. Beyond that, the atmospheric moisture budget equation is fundamental for cloud-seeded modeling which is confirmed by the results. Hence, the central parts of the study area have a maximum value of P and a minimum value of T which is a mountainous area. The ECMWF data is well agreed to the ground-based observation over arid and semiarid regions of Ethiopia. Hence, ECMWF model data is used for cloud-seeded modeling. Based on error metrics the performance of the cloud-seeded model is well confirmed with direct cloud-seeded operation in the study period and study area. Indeed, the cloud-seeded technology is effective over Ethiopia when the daily resultant wind speed is less than 1.5 m/s and CBH is less than 1700 m. In line with this, relatively spring and slightly autumn seasons are recommended to practice cloud-seeded operation. The RP for spring, summer, and autumn is found 1.31, 0.98, and 1.03 respectively. During spring 2021, the effectivity of cloud-seeded technology is found 28.79%; while the literature value until this time is around 25%. Hence, this paper considered the atmospheric moisture budget for cloud-seeded modeling, increasing its effectivity by around 3.79%. Hence, we recommended that the government of Ethiopia should be nowcasting and forecasting the daily wind speed before the cloud-seeded operation is practiced. The cloud-seeded model and technology are not recommended during the summer seasons because RP is less than 1. Clear awareness should be delivered to researchers, policymakers, and governmental and non-governmental organizations for cloud-seeded model improvement and to produce a new cloud formation model that is suitable to arid and semiarid regions of Ethiopia for precipitation enhancement and to implement a sustainable future.

Declaration

Author contribution statement

Megbar Wondie: Conceived and designed the experiments; Performed the experiments; Analyzed and interpreted the data; Contributed reagents, materials, analysis tools or data; Wrote the paper.

Funding statement

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Data availability statement

The data that has been used is confidential.

Declaration of interest’s statement

The authors declare no conflict of interest.