An underground heat storage system with water as a heat transport and storage medium of solar heat inside a greenhouse was studied to develop a highly efficient and inexpensive solar greenhouse.
The system was designed and installed in a small experimental greenhouse used for basic research. The greenhouse was plastic double covered and the floor area was 28.9m². The heat exchanger, which had 23m² of effective heat transfer area and was made of a polyethylene film, and which had been developed for a greenhouse heating with moderate heat sources was installed to transfer heat between water and air inside the greenhouse. PVC pipes (diameter of 48mm, wall thickness of 2mm, and total length of 58m) were buried under the greenhouse floor at 0.35m deep and 0.35m apart to store heat in the soil around the pipes. The pipes and a tank under the heat exchanger were filled with 0.27m³ of water, the water was circulated through the pipes and heat exchanger to transport heat and also to store heat in itself. No crop was cultivated in the greenhouse.
The storage efficiency of solar heat (daily solar storage/daily ambient solar heat) ranged from 0.08 to 0.12. The water contributed 37 to 48% of all the heat storage. The heat released for night-time heating ranged from 42 to 86% of the solar storage.
The heat transfer coefficient from the water inside the pipes to the outside pipe surface was 53-60kcal/m² h°C (based on the outside pipe surface), this value agreed with the theoretical value. From the temperature distributions around the pipes, the soil which acted as the heat storage layer was within 17.5cm. As a consequence of the result, the heat transfer coefficient between the water and the soil at 17.5cm under the pipes (Ks) indicates the total heat flow rate of the underground pipe system, and was 10.6kcal/m²h°C on the average except for times when the direction of the heat flow at the pipe surface reversed.
The overall heat transfer coefficient between the air inside the greenhouse and the soil at 17.5cm under the pipes (Kt) was defined by using the heat transfer coefficient of the heat exchanger, 16kcal/m²h°C (Okano et al., 1980), and Ks. Kt was 3.2 kcal/m²h°C (based on the heat transfer area of the heat exchanger), and was predicted to increase to 4.6kcal/m²h°C when the surface of the pipes was extended in 14.43m² (half of the floor area) by extending the diameter of the pipes to 87mm. The system is thus assumed to achieve performance similar to an underground heat exchange system equipped with the same pipe surface as a greenhouse floor area, and having the common heat transfer coefficient (5kcal/m²h °C, Yamamoto, 1981) between the air inside the pipes and the soil around the pipes.

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The method to obtain the detailed map of the maximum snow depth is developed on the basis of the mesh-square technique. It is applied to planning and design for fruit-tree cultivation in the Hokuriku District, where heavy snowfall occurs in winter. Results are summarized as follows:
1) Statistical relationships were established between the maximum snow depths and topographical land features for selected 58-point locations in the Hokuriku District. The third degree orographic mesh data was used to determine the topographical parameters which, in turn, were related to the maximum snow depth on average (S_{h, m}, cm), on five-year return periods (S_{h, 5}, cm), on ten-year return periods (S_{h, 10}, cm) and on twenty-year return periods (S_{h, 20}, cm). They were respectively given by following relations:
S_{h, m}=119+2.060H_m+0.182K+2.05√M_{h, r15}+0.021K_{s, r1}+1.729R_{e, r10}-2.548R_{d, r5}-3.056√P_{10, r15}, (r=0.94) (3)
S_{h, 5}=211+13.36 ln (H_m)+11.59ln(P_{20, r5})+0.060K_{s, r1}+0.222K-2.37R_{d, r5}-7.03√P_{60, r13}-2.67√R_{d, r10}-5.86√S_r15, (r=0.93) (4)
S_{h, 10}=224+0.299K+6.21√M_{h, r15}+3.53H_{a, r1}-0.215H_m-2.63R_{d, r5}-5.30√P_{10, r15}-0.191 (S_{100, r2})² (r=0.94) (5)
S_{h, 20}=260+0.301I+11.15√M_{h, r15}+1.34H_{a, r1}+0.051K_{s, r1}+10.15 ln (P_{20, r5})-4.03R_{d, r5}-8.17√P_{10, r15}-7.59√M_{h, r11} (r=0.93) (6)
where each predictor variable is defined in Table 1.
2) Relatively large errors of estimate, around 20% of the observed values, were obtained for several stations. The regressions used here overestimated the maximum snow depths for the adjacent regions of isolated mountains in the coastal plain, the Noto Peninsula and most inland areas of the Hokuriku District. On the other hand, underestimates of them were obtained for those stations in inland hilly countries. Such deficiency suggests that the accuracy of estimate is not satisfactory if different climates are included in a given area of interest.
3) The detailed geographical distribution of the maximum snow depth was then estimated from these multiple regression equations with the orographic mesh data as inputs. An example of the distribution as mapped using all the points of about 1km² grid in the basal part of the Noto Peninsula is illustrated in Fig. 4.
4) Agroclimatic classification criteria for fruit-tree cultivation planning based on the maximum snow depths were made from the survey data on physical snow damages reported for the Hokuriku District (Table 2). This result was superimposed on the mesh distribution of the maximum snow depth to construct the land use planning for fruit-tree cultivation (Fig. 6).
The map thus obtained indicates not only the design criteria for a normal climatological period but also the probability of occurrence of snow damage on fruit trees.

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A simple model for the dry surface layer of sand under isothermal steady conditions was formulated, and an experiment was conducted to verify the model, using the Tottori Dune sand. The experimental results show that the proposed model yields the essential features of the dry surface layer.
The present model can relate the thickness of the dry surface layer to the soil temperature and the upward water flux in the layer. The flux can be identified with the evaporation rate, which is important from a practical point of view but is difficult to measure. Thus, this model could be used to estimate the evaporation rate in a sand dune field from the thickness and the temperature of the dry surface layer.

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The growth and development of a plantlet in vitro may be affected by the gas environment in a vessel. The gas micro-environment in the vessel may be in turn influenced not only by the generation and absorption of gas by the plantlet and the culture medium, but also by the gas exchange between the room air and the air in the vessel. The gas exchange rate between the room air and the inside air may be varied with the combination of a vessel and its stopper.
This paper describes a method for estimating the number of air changes per hour and the coefficient of gas exchange for various vessels closed with various stoppers. The measured values are also given for various vessels closed with various stoppers.
In the measurement, carbon dioxide gas was used as tracer gas. The gas concentration inside and outside the vessel was measured by using a gas chromatograph at a certain time interval.
Three kinds of stoppers were tested. Namely, aluminium foil cap C_A, plastic formed cap C_P, and silicon foam rubber plug P_S. Six kinds of vessels were tested. Three of them were glass test tubes and the rest were glass Erlenmeyer flasks (see Tables 1, 2 and Fig. 1).
The number of gas changes per hour estimated by using carbon dioxide gas as tracer gas, E_c, was estimated by the following equation:
E_c=-1/T⋅lnK-K_ou/K₀-K_ou
where T is the time interval from time 0 to time t, K the gas concentration at time t, K₀ the gas concentration at time 0, K_ou the gas concentration outside the vessel.
The coefficient of line carbon dioxide gas exchange, q_cc, and the coefficient of area carbon dioxide gas exchange, q_ac were, respectively, estimated by the following equations:
q_cc=V⋅E_c/C, q_ac=V⋅E_c/A
where V is the inside air volume of the vessel, C the inside circumference of the vessel, and A the inside area at the lip.
q_cc is used for the vessel closed with the stopper with no gas permeability, such as aluminium foil cap and plastic formed cap. q_ac is used for the vessel closed with the stopper with a certain degree of gas permeability, such as silicon foam rubber plug.
The number of gas changes per hour estimated by using carbon dioxide gas as tracer gas, E_c, for various vessels closed with various stoppers is shown in Table 3. The variations of E_c for different vessel-stopper combinations were considerable. As for the vessels closed with the same material of stopper, E_c was inversely proportional to the inside air volume of the vessel. As for the same vessel closed with different caps, E_c was the largest for C_P, and the smallest for C_A.
The coefficients of carbon dioxide gas exchange for various vessels closed with various stoppers, q_cc and q_ac, are given in Table 4. q_cc and q_ac showed a rather constant value for different vessels closed with the same stopper, regardless of the large variations of the inside air volume of the vessels. q_cc of C_P was about 10 times larger than that of C_A for the same vessel.
It is shown that the number of gas changes per hour and the gas exchange rate between the room air and the inside air are considerably affected by the vessel-stopper combination. Therefore, the concentration of a gas, and hence the growth and development of a plantlet in vitro may be affected by the vessel-stopper combination.
The method proposed in this paper may be applied to any gas such as ethylene gas, water vapor, oxygen gas etc., in principle, if the corresponding gas is used as tracer gas.

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In estimating air temperature using ground temperature from GMS IR data, ground effect corrections are equally important as atmospheric effect corrections. However, ground effect corrections are very difficult to conduct, requiring a great amount of analyses because of complicated relationship between air temperatures and ground temperatures.
In our previous study (Tani et al, 1984), it was found that the classification of AMeDAS meteorological sites using deviation between temperatures estimated from the GMS IR data and those obtained from AMeDAS data, indicated that meteorological sites of the same type tend to form groups. This shows that the accuracy of temperature estimations increases when the estimation is carried out by small districts. The classification of AMeDAS meteorological sites in Hokkaido was conducted by cluster analysis using temperature deviations. The following two kinds of data were used for the cluster analysis: sixty-one GMS IR data and AMeDAS data collected from 1979 through 1984. Mean deviations between the temperatures estimated from GMS IR data obtained at three hour intervals and those obtained from AMeDAS data were calculated. Using these deviations, cluster analyses of AMeDAS meteorological sites were made. The results are shown in Fig. 2. Furthermore, AMeDAS meteorological sites were classified based on the deviations between the time average temperatures of AMeDAS and the time temperature of AMeDAS. The results are shown in Fig. 3.
Using the results of this classification, temperatures of the five districts were estimated, shown in Figs. 2 and 3. The temperature estimations of the five districts were conducted using four methods (six different calculation methods) and the accuracies clarified.
1) The regression equation of surface temperature from GMS data and AMeDAS temperature was calculated throughout Hokkaido island. The regression equation was applied to five districts. (named One-equation method)
2) The regression equations were calculated for each cluster. The regression equations were applied to the clustered meteorological sites in five districts. The following two kinds of data were used for the cluster analysis.
a) GMS IR data.
b) AMeDAS data.
(named Cluster method)
3) The regression equations were calculated for each five district. (named District method)
4) AMeDAS meteorological sites were selected based on the classification results of Figs. 2 and 3 for each five district. The regression equations were calculated for those groups. The classifications in Figs. 2 and 3 were calculated using following two data.
a) GMS IR data.
b) AMeDAS data.
(named District-cluster method)
Table 1 shows the estimation accuracy of different calculation methods. The calculation results of method 4 (District-cluster method) were the most accurate with an error of 1.0±0.1 (K).

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How the flow responds to the various types of surface inhomogeneity is an important problem in micrometeorology. In this study the distributions of wind velocity over a pulse change in surface roughness where smooth-to-rough and rough-to-smooth changes occur consecutively under neutral conditions are investigated experimentally and numerically. Mean wind velocities and their fluctuations over a short length of surface roughness are measured in the wind tunnel experiments. A two dimensional numerical model is developed to predict the distributions of mean velocity over a pulse change in surface roughness. In the model the present arrangement of roughness elements is assumed to be a mixture of two kinds of sudden changes of surface roughness. Though the model uses a simple mixing length theory, the calculated results generally agree with the experimental data. Measured response of Reynolds stresses to the roughness is also shown. Distributions of these stresses show the state of generation of turbulence by the roughness elements and effects of the pulse change in roughness on the downstream flow.

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